The Advantages and Disadvantages of Computer Technology in Second Language Acquisition

Cheng-Chieh Lai
PhD Student in Educational Leadership
Prairie View A&M University
College of Education
William Allan Kritsonis, PhD
Professor
PhD Program in Educational Leadership
Prairie View A&M University
Member of the Texas A&M University System
Visiting Lecturer (2005)
Oxford Round Table
University of Oxford, Oxford, England
Distinguished Alumnus (2004)
Central Washington University
College of Education and Professional Studies

ABSTRACT
The purpose of this article is to discuss the advantages and disadvantages of
computer technology for second language learning. Research findings indicate that
the use of computer has a positive effect on the achievement levels of second
language learners, but it still has its limitations and weaknesses, such as financial,
isolated, and knowledge required issues. The authors emphasize that we must
recognize both the advantages and disadvantages of using computers so we can get
the maximum effectiveness of technology to enhance second language learning.
inspired by the rapid development of technology from the 1980s, computer has now
become an influential component of second language learning pedagogy. Educators
recognize that utilizing computer technology and its attached language learning
programs can be convenient to create both independent and collaborative learning
environments and provide students with language experiences as they move through the
various stages of second language acquisition (Kung, 2002).
The purpose of this article is to discuss the advantages and disadvantages of computer technology applied in current second Language instruction. In second language
acquisition domain, Perrett (1995) has mentioned that if students are provided with the
opportunities to use language and learning strategies in the second language, and some
training or explanation in their application, they can develop these strategies through
exposure to and experience in the second language.” Therefore, explaining the
advantages and disadvantages of computer technology to teachers and students seems to
be necessary. Only after guiding, do teachers and students realize the benefits of
computer technology for second language acquisition, then they can apply computer
appropriately and join those computer assisted language learning programs by their own wills.

Advantages of CALL Programs
Educators (Jonassen, 1996; Salaberry, 1999; Rost, 2002) indicate that the current
computer technology has many advantages for second language learning. Computer and
its attached language learning programs could provide second language learners more
independence from classrooms and allowing learners the option to work on their learning
material at any time of the day. Once implemented, it can be expected that the cost for
computer technology is considerably lower than for face-to-face classroom teaching, and
when used in conjunction with traditional second language classroom study, students can
study more independently, leaving the teacher more time to concentrate effort on those
parts of second language teaching that are still hard or impossible by the computer, such
as pronunciation, work on spoken dialogue, training for essay writing and presentation
(Roger, 1996).
Lee (2000) further stated that the reasons why we should apply computer
technology in second language instruction, include computer and its attached language
learning programs can (a) prove practices for students through the experiential learning,
(b) offer students more the learning motivation, (c) enhance student achievement, (d)
increase authentic materials for study, (e) encourage greater interaction between teachers
and students and students and peers, (f) emphasize the individual needs, (g) regard
independence from a single source of information, and (h) enlarge global understanding.
Taylor (1980) also expressed that computer assisted language learning programs can be
wonderful stimuli for second language learning. Currently, computer technology can
provide a lot of fun games and communicative activities, reduce the learning stresses and
anxieties, and provide repeated lessons as often as necessary. Those abilities will promote
second language learners’ learning motivation. Through various communicative and
interactive activities, computer technology can help second language learners strengthen
their linguistic skills, affect their learning attitude, and build their self-instruction
strategies and self-confidence. According to Robertson et al. observation (1987), the
participants who joined computer-assisted language learning programs also had
significantly higher self-esteem ratings than regular students.
Today, with the high development of computer technology, computers can capture,
analyze, and present data on second language students’ performances during the learning process. As we know, observing and checking students’ learning progress are very
important activities to help students achieve their second language acquisition. When
teachers attempt to assess students’ learning progress, they can get the essential
information from a well-designed computer language learning programs and then offer
feedback tailored to students’ learning needs (Taylor & Gitsaki, 2003). In addition,
Students can get various authentic reading materials either at school or from home by
connecting to the Internet. And, those materials can be accessed 24 hours a day. In a word,
computer technology also provides the interdisciplinary and multicultural learning
opportunities for students to carry out their independent studies.
For learning interaction, Warchauer (2004) indicated that the random access to
Web pages would break the linear flow of instruction. By sending E-mail and joining
newsgroups, second language learners can also communicate with people they never met
before and interact with their own teachers or classmates. Shy or inhibited learners can be
greatly benefited through the individualized technology-learning environment, and
studious learners can also proceed at their own pace to achieve higher levels.
In particular, many concepts and cognitions are abstract and difficult to express
through language the language teaching area. It seems that computers can make up for
this shortage by using the image showing on the screen. Nunan (1999) reported that
“interactive visual media which computers provided seem to have a unique instructional
capability for topics that involve social situations or problem solving, such as
interpersonal solving, foreign language or second language learning” (p.26).
Both cognitive theorists and humanists all pointed out that practice experience is a
very important factor for people’s learning. Experiential theory educators believe that
learning is about making sense of information, extracting meaning and relating
information to everyday life and that learning is about understanding the world through
reinterpreting knowledge (Ormrod, 1999). When computer technology combines with
Internet, it creates a channel for students to obtain a huge amount of human experience
and guide students to enter the “Global Community”. In this way, students not only can
extend their personal view, thought, and experience, but also can learning to live in the
real world. They become the creators not just the receivers of knowledge. And, “as the
way information is presented is not linear, second language learners can still develop
thinking skills and choose what to explore” (Lee, 2000).

Disadvantages of CALL Programs
First, although there are many advantages of computer, the application of current
computer technology still has its limitations and disadvantages. Gips, DiMattia, & Gips
(2004) indicated that the first disadvantage of computer and its attached language
learning programs is that they will increase educational costs and harm the equity of
education. When computers become a basic requirement for student to purchase, low
budget schools and low-income students usually cannot afford a computer. It will cause
unfair educational conditions for those poor schools and students. On the other hand,
expensive hardware and software also becomes the big obligations for schools parents.
Second, it is necessary that both teachers and learners should have basic
technology knowledge before they apply computer technology to assist second language
teaching and learning. No student can utilize computer if he or she lacks training in the
uses of computer technology. Unfortunately, most teachers today do not have sufficient
technological training to guide their students exploring computer and its assisted
language learning programs. Therefore, the benefits of computer technology for those
students who are not familiar with computer are inexistent (Roblyer, 2003).
Third, the software of computer assisted language learning programs is still
imperfect. Current computer technology mainly deals with reading, listening, and writing
skills. Even though some speaking programs have been developed recently, their
functions are still limited. Warschauer (2004) pointed out that a program should ideally
be able to understand a user’s “spoken” input and evaluate it not just for correctness but
also or “appropriateness”. It should be able to diagnose a student’s problems with
pronunciation, syntax, or usage and then intelligently decide among a range of options.
Fourth, computers cannot handle unexpected situations. Second language
learners’ learning situations are various and ever changing. Due to the limitations of
computer’s artificial intelligence, computer technology is unable to deal with learners’
unexpected learning problems and response to learners’ question immediately as teachers
do. The reasons for the computer’ inability to interact effectively can be traced back to a
fundamental difference in the way humans and computers utilize information (Dent,
2001). Blin (1994) also expressed that computer technology with that degree of
intelligence do not exist, and are not expected to exist for quite a long time. In a word,
today’s computer technology and its attached language learning programs are not yet
intelligent enough to be truly interactive. People still need to put effort in developing and
improving computer technology in order to assist second language learners.

Concluding Remarks
In conclusion, the purpose of this article was to discuss the advantages and
disadvantages of CALL programs for applying in current ESL classrooms. With the
modern technology development, although the application of CALL programs has
become a new trend in recently global second language learning instructions, computer
technology still has its limitations and weakness. Therefore, when we try to apply CALL
programs to enhance their teaching or to help student learning, we should realize what the
advantages and disadvantages are in current CALL programs in order to avoid for
misemploying CALL programs and get its maximum benefits for our ESL teaching and
learning.

References
Blin, F. (1999). CALL and the Development of Learner autonomy. In R. Debski and M.
Levy (eds.), WorldCALL: Global perspectives on computer-assisted language
learning, lisse: Swets and Zeitlinger, pp.133-147.
Dent, C. (2001). Studer: classification v. categorization. Retrieved June 28, 2006, from
http://www.burningchrome.com:8000/~cdent/fiaarts/docs/1005018884:23962.html.
Gips, A., DiMattia, P., & Gips, J. (2004) The effect of assistive technology on educational
costs: Two case studies. In K. Miesenberger, J. Klaus, W. Zagler, D. Burger (eds.),
Computers Helping People with Special Needs, Springer, 2004, pp. 206-213.
Jonassen, D.H. (1996). Computers in the classroom. Englewood cliffs, NJ: Merrill.
Kung, S. C. (2002). A framework for successful key-pal programs in language learning,
CALL-EJ Online, 3 (2). Retrieved June, 20, 2006, from
http://www.clec.ritsumei.ac.jp/english/callejonline/6-2/SCKung.htm
Lee, K.W. (2000). English teachers’ barriers to the use of computer assisted language
learning, The Internet TESL Journal. Retrieved June, 25, 2006, from
http://www.4english.cn/englishstudy/xz/thesis/barrir
Nunan, D. (1999). Second language teaching & learning. Boston: Heinle & Heinle
Publishers.
Ormrod, J.E. (1999). Human Learning (3rd Edition). Upper Sadle River, NJ: Merrill
Prentice Hall.
Perrett, G. (1995), August Communicative language teaching and second language
acquisition theory. Paper delivered at 1993 MLTA Conference. Published in MLTAQ
Inc. Newsletter No. 101.
Robertson, E. B.; Ladewig, B. H.; Strickland, M. P., & Boschung, M. D. (1987).
Enhancement of self-esteem through the use of computer-assisted instruction.
Journal of Educational Research, 80 (5), 314-316.
Roblyer, M. (2003). Integrating educational technology into teaching. Columbus, Ohio:
Person Education
Rost, M. (2002). New technologies in language education: Opportunities for professional
growth. Retrieved June 28, 2006, from
http://www.longman.com/ae/multimedia/pdf/MikeRost_PDF.pdf
Salaberry, R. (1999). CALL in the year 2000: Still developing the research agenda.
Language Learning and Technology, 3 (1), 104-107.
Taylor, R. (1980). The computer in the school: Tutor, tool, and tutee. New York: Teachers
College Press.
Taylor, R. & Gitsaki, C. (2003) Teaching well and loving it. In Fotos & Browne (Ed.),
New perspectives on CALL for second language classrooms (pp. 131-147). Mahwah,
NJ: Lawrence Erlbaum Associates.
Underwood, J. (1984). Linguistics, computers, and the language teacher: A
communicative approach. Rowley, MA: Newbury House.
Warschauer, M. (2004). Technological change and the future of CALL. In Fotos &
Browne (Ed.), New perspectives on CALL for second language classrooms (pp.
15-26). Mahwah, NJ: Lawrence Erlbaum Associates.
Formatted by Dr. Mary Alice Kritsonis, National Research and Manuscript Preparation
Editor, NATIONAL FO

Corpus Linguistics, Concordance and Data-Driven Learning: An innovative Language Teaching Approach!

Technorati Tags: concordance,data-driven learning,corpus linguistics
The Corpus linguistics refers to a body of text. This text can be written or spoken or a combination of both. Corpora ( plural of corpus) can be based on brief text on a narrow topic or can run into millions of words such as BNC ( British National Corpus, a 100- million words of British English) or Cobuild Corpus.
To access, or make use of a corpus one should use a concordancer to look at linguistic patterns. A concordancer is a software that show instances of words in a body of text. In addition, it allows to show collocations and frequencies of words. This approach can be called Key word in context ( KWIK). Now web-based concordancers are being increasingly available, such as Cobuild and lextutor.
The following screenshots are from the Cobuild web-based Corpus
1. Write the word you want to query in the box, then click “show conc”

2. A pop-up window will appear with the instances of the word
Click here to view it ( better than providing a screenshot :)
3. To use in the classroom, teacher should turn students’ attention to the collocations and usage of the word in authentic language ( since the word is retained from authentic text and not text made specifically for esl/efl). Students then can derive grammatical rules (eg. modal verbs, indefinite pronouns..) and notice how certain vocabulary is used in authentic context. They can deduce what a certain vocabulary collocates with.

4. The teacher can also use a fill-in-the-blank activity where the word in query is omitted. If internet connection is not available or there are no computers in the classroom, the teacher can distribute them as printout. Note that the teacher should spend time preparing this concordance before presenting it in the classroom to ensure that it targets the intended language use.


Using corpora in the classroom involves making use of concordance software to analyze a corpora ( or web-based concordance such as the above example) and spot patterns and differences in language usage. For instance, students can use corpus linguistics with the aid of a concordance to make error corrections to their writing, or the teacher can show students a certain syntactical or lexical usage for students to induce the rule ( inductive learning), called data-driven learning since it is based on a data analysis that results in linguistic learning. ( check out the father of data-driven learning website, Tim Jones). For more on the idea of data-driven learning click here. Of course, using a concordance and corpora is not easy for students to handle so it is imperative that students practice extensively on deriving or inducing rules from linguistic patterns, or even correct their linguistic and writing error based on a written corpus.
Again, it is important to note that data-drive learning demands extensive practice before employing it as an approach. The role of the teacher becomes that of a manager, orienteer, and observer and the role of the student changes to a researcher of language.
Why use this approach instead of traditional grammar and lexical instruction?
• It exposes the language learner to authentic language instead of rather fabricated ESL text
• It changes the role of the language learner from a mere receptive individual into a language researcher ( note that this approach might not work as expected especially with young learners).
• It ensures a learner-centered classroom without diminishing the role of the teacher
• It encourages learner autonomy with regard to errors correction ( will be discussed in my next post)
More posts will also discuss more on concordancing, data-driven learning, and corpora. How a teacher can collect a certain corpus for a certain learning context, how a teacher can analyze his/her learners’ linguistic output, such as writing, called learner error analysis,and how to use corpora in more activities in the classroom.
Now, I leave you with some links to concordance software, including web-based, that you can use and play around:
• antconc Lawrence Anthony’s free concordance software that you can download
• monoconc pro commercial concordance
• concordance commercial concordance software that I use
• lextutor a free web-based concordance
• Cobuild free web-based concordance and corpus

Computer-Assisted Language Learning and the Revolution in Computational Linguistics

Pius ten Hacken (Swansea)
Abstract
For a long period, Computational Linguistics (CL) and Computer-Assisted Language
Learning (CALL) have developed almost entirely independently of each other. A brief
historical survey shows that the main reason for this state of affairs was the long
preoccupation in CL with the general problem of Natural Language Understanding (NLU). As a consequence, much effort was directed to fields such as Machine Translation (MT), which were perceived as incorporating and testing NLU. CALL does not fit this model very well so that it was hardly considered worth pursuing in CL. In the 1990s the realization that products could not live up to expectations, even in the domain of MT, led to a crisis. After this crisis the dominant approach to CL has become much more problem-oriented. From this perspective, many of the earlier differences disadvantaging CALL with respect to MT have now disappeared. Therefore the revolution in CL offers promising perspectives for CALL.
1 Introduction
Computer-Assisted Language Learning (CALL) is the field concerned with the use of
computer tools in second language acquisition. Somewhat surprisingly, perhaps, this field has never been closely related to Computational Linguistics (CL). Until recently, the two fields were almost completely detached. Despite occasional attempts to apply techniques of Natural Language Processing (NLP) to the recognition of errors, NLP in CALL has long remained in a very small minority position while CALL was hardly if at all recognized as a part of CL. In
this contribution, I intend to show how CL could remain largely irrelevant to CALL for such a long time and why there is a good prospect that this will change in the near future. Section 1
describes the situation of CL before the revolution. In section 2, the crisis leading to the revolution in CL is outlined. The revolution itself is the topic of section 3. The implications for the field are then sketched in section 4. Finally, section 5 summarizes the conclusions.
2 Computational Linguistics as Natural Language Understanding
CL is almost as old as the first working computer. In fact, at a time when computer science was still in its infancy, Weaver (1955 [1949]) had already proposed the use of computers for translation, thus initiating research in Machine Translation (MT). Weaver considered two approaches to MT, one based on linguistic analysis and the other on information theory.

Neither of these could be implemented at the time of Weaver's proposal. Information theory had been more or less fully developed by Shannon (1948), but its application to MT required computational power of a magnitude that would not be available for several decades.
Linguistic analysis appeared more promising, because it can be performed with considerably less computational power, but the theoretical elements necessary for its successful application were still missing. Thus much work in early CL was devoted to developing the basic mechanisms required for linguistic analysis.
One of the first types of knowledge to be developed concerns the computational properties of formalisms to be used in the description of languages. In response to this requirement, the theory of formal grammars was developed, mainly in the course of the 1950s. Noam Chomsky played an active role in systematizing and extending this knowledge and Chomsky (1963) provides an early, fairly comprehensive overview of the properties of grammars consisting of rewrite rules of the general type as in (1).
(1) →
In this approach, a formal description of a language consists of a set of rules in which and  in (1) are replaced by strings of symbols. When designed properly, such a system of rules is able to generate sentences. If we consider a language as a set of sentences, we can see the grammar as a definition of the language. Different types of grammar impose different conditions on and . Thus, if in all rules of a grammar is not shorter than , it can always be determined by a finite procedure whether a given sentence belongs to the grammar or not.
For Context-Free Grammars (CFGs), in which in (1) is a single symbol in each rule, the structure can be represented as a tree diagram.
The next step on the road to linguistic analysis in CL was the development of parsers. A parser is an algorithm to determine for a given sentence x and a grammar G whether G can generate x and which structure(s) G assigns to x. Ground-breaking work in this area was done in the 1960s with the development of the chart parser (cf. Varile 1983 for an overview), Earley's (1970) efficient parser for CFGs, and the more powerful Augmented Transition Networks of Woods (1970).
With a grammar formalism and a number of parsing algorithms in place, the only missing link to successful linguistic analysis was the description of the relevant languages. As it turned out, however, this problem was more recalcitrant than the other two. Chomsky developed a theory of grammar using formal rules of the type in (1), but his theory is less congenial to CL than may appear at first sight. Chomskyan linguistics has often been considered as based on a concept of language as a set of sentences and some remarks by Chomsky (1957) can be taken to support this view. At least from the early 1960s onwards, however, Chomsky has consistently and explicitly rejected such a view in favour of language as a knowledge component in the speaker's mind. Chomsky (1988) gives an accessible explanation and justification of the assumptions underlying this general approach and the type of linguistic theory it leads to.
Pius ten Hacken: CALL and the Revolution in CL Given this approach to language, there is no convergence in goals between Chomskyan linguistics and CL. Whereas the former is interested in describing and explaining a human being's knowledge of language, the latter is interested in processing the products of language
use on a computer. An example of this divergence is the reaction to the realization that transformational rules of the type used in Chomsky (1965) are excessively powerful. This excessive power appears both in language acquisition on the basis of input sentences and in language processing leading to the understanding of sentences and utterances. In Chomskyan
linguistics it was not the processing complexity but only the learnability requirement of the
grammar which drove the restriction of transformations. Chomsky's linguistic theory
continued to involve movement operations defined over nodes in a tree structure. In analysis,
this requires the 'undoing' of movement, which is a computationally complex operation.
Processing complexity of grammars produced in the Chomskyan framework has remained a
major problem for their computational implementation, but this does not and need not
inconvenience Chomskyan linguists. From the perspective of Chomskyan linguistics, as
language is a typically human property, it is quite plausible that the human mind is structured so as to facilitate processing of the type necessary for human language. A computer does not have this structure.
From the 1970s onwards, a number of alternative linguistic theories have been developed with the computational implementation in mind. At present, the most influential ones are Lexical-Functional Grammar (LFG, cf. Bresnan 2001) and Head-Driven Phrase Structure Grammar (HPSG, cf. Pollard/Sag 1994). They still use rewrite rules of type (1) to some
extent, but their actual formal basis is the unification of feature structures. Feature structures
can be seen as sets of attribute-value pairs describing individual nodes in a tree structure. The formal device of feature structures and the operations on them were developed in full only in the 1980s. An early overview is Shieber (1986). By applying operations such as unification to feature structures, movement of nodes in a tree can be dispensed with. This is important for CL, because operations of this type are much more computer-friendly than undoing movement.
Given this historical development, it is understandable why for a long time research in CL, a significant part of which was at least in name devoted to MT, largely coincided with research in natural language analysis, i.e. parsing techniques and formal linguistic description. Work on different applications (e.g. MT, dialogue systems, text summarization) did not lead to major divisions in the CL research community, because in all such applications analysis was
considered as the logical first step. This attitude is reflected in Kay's (1973) proposal of a modular system of natural language understanding, the parts of which could be connected in
different ways depending on the requirements of the application.
If major divisions in the CL research community could not be identified on the basis of different applications, one might wonder whether there was any other source of major divisions. Most of the discussions in CL turned on issues such as the choice of linguistic theory, formalism, and parsing strategy. Although in the perception of people working in the field, different positions on these issues led to a division into competing currents of research,they should not be confused with major divisions in the field. All of these currents were
basically geared towards the same task and their success could be compared directly. This contrasts with the situation in theoretical linguistics as described in ten Hacken (1997), where Chomskyan linguistics and LFG propose different, competing research programmes, whose results are often incompatible in a way that defies an evaluative comparison.
In this context it is interesting to see that in the perception of many computational linguists, work in CL was not essentially different from work in theoretical linguistics. Thus Thompson (1983) states that theoretical linguistics aims to characterize a language and CL proper aims to do so computationally. These were especially anti-Chomskyan linguists interested in grammar
and language processing. Rather than concentrating on MT for its own merits, they were working on natural-language understanding (NLU). Concrete applications, among which MT was prominent, served on the one hand as a test of whether the goal of NLU, i.e. making a computer understand human language, had been achieved and on the other hand to convince funding sources of the practical use of their enterprise.
At this stage there was little interest in CALL among CL-practitioners, which can be
explained by the orientation to NLU. Whereas the translation into another language reflects the degree of understanding of a sentence achieved by the computer fairly directly, the relationship between NLU and CALL is much more complex. Conversely CALL could not readily incorporate results obtained in CL. Work in NLU starts from the assumption that the sentences to be analysed are grammatical. Much of the analysis in CALL is actually concerned with establishing whether sentences are grammatical and appropriate and, if not, how they can be corrected. Advances in NLU were thus largely irrelevant to CALL.
The use of the computer in CALL in this period, as described by Levi (1997) in his historical overview, was determined to a considerable extent by general-purpose computing and text editing. Two types of application illustrating typical techniques are the vocabulary trainer and
the generator of cloze tests. A vocabulary trainer is a system for the management of a bilingual vocabulary list. It presents a word in one language and prompts the user to enter the corresponding word in the other language. It checks whether the word entered is correct, gives the appropriate feedback, and stores the result. The order of presentation can be randomized and made to take into account the user's progress in vocabulary acquisition.
Nesselhauf/Tschichold (2002) give an evaluative overview of a number of commercially
available products of this type. The techniques involved are restricted to general pattern matching and database management, without any specifically linguistic components.
A cloze test is a sequence of sentences with spaces for the language learner to fill in.
Examples are exercises for the endings of articles and adjectives in German or the translation
of ambiguous words in context. Their generation on the basis of a full text can be done by an
authoring tool which prompts the teacher to import a text, indicate the words or parts of words
to be deleted, and if necessary add a question or hint for the learner as to the word to be
entered. The test can then be taken and corrected electronically. Interface design and pattern
matching are again the basic techniques used.
Pius ten Hacken: CALL and the Revolution in CL
ISSN 1615-3014
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3 The Crisis in CL and the Emergence of Information Theory
In its NLU orientation, CL was closer to theoretical linguistics than to actual applications.
This was not only evident in areas such as CALL, considered peripheral to mainstream NLU,
but also in core applications such as MT. With the gradual advance of CL research as
sketched above, the gap between mainstream research and practical applications increased.
In the 1950s and early 1960s, before the availability of advanced parsing technology for
natural language, state-of-the-art MT systems were in a similar position to CALL until much
more recently. They were based on word-for-word substitution supplemented by a number of
general computational techniques. Among these systems, Systran is no doubt the best
documented and most commercially successful example. Although Toma (1977) and Trabulsi
(1989) emphasize the difference between Systran and early word-for-word MT systems, an
important similarity is the absence of a full parse in the translation process. Instead, local
pattern matching is used to identify relationships between words which influence the
operations to be carried out on them. Dictionary lookup introduces the target language (TL)
equivalents to the source language (SL) words as one of the first steps. Before any syntactic
analysis is attempted, a homography resolution component tries to reduce ambiguity resulting
from bilingual dictionary lookup on the basis of the words immediately preceding or
following the ambiguous word. Subsequent steps aim to reduce ambiguity, but only at the
very end do such operations as readjusting adjective placement in English to French
translation take place. There is no representational level or operational component of NLU in
Systran or similar systems.
With the increasing sophistication of parsers and linguistic theory, expectations as to the
performance of MT were raised. It became increasingly embarrassing for people working in
MT to claim that success was just around the corner, pending some further advances in
parsing technology and linguistic analysis. Yet, in terms of practical output, the results of
NLU-based MT were disappointing.
A good illustration of the gap between research effort and practical use in linguistically based
MT is the experience of the Commission of the European Communities (CEC). As described
by Wheeler (1987), the CEC purchased its first Systran translation system, English to French,
in 1975 and gradually increased the number of language pairs and the size of the dictionaries
for them. As described by Maegaard/Perschke (1991), the planning phase for the CEC's own
MT project, Eurotra, started soon after the purchase of Systran, in 1977. Eurotra was intended
from the start to eventually replace Systran. Using state-of-the-art linguistic analysis, the idea
was to represent the SL sentence at such an abstract level that the transfer to the
corresponding abstract TL representation would be minimal. This means that strictly
monolingual analysis is maximized in order to reduce bilingual transfer. This is an advantage
in a multilingual environment because with nine languages there are nine analysis modules,
but 72 transfer components.
Despite these theoretical advantages and significant investment in Eurotra over the period
1982-1992, in their overview of current translation work in the European institutions Wagner
et al. (2002) do not mention Eurotra or any of its offshoots. The index has an entry "machine
translation. See Systran" and on the relevant pages it is described how a proprietary version of
Systran is now used for information scanning on the basis of raw translation, as a source of
rapid post-editing, and as an optional aid to full quality translation.
In the late 1980s the divergence between expectations and the possibility of delivery reached
a point where the field entered into a state of crisis. As is typical in such a situation, one finds
a number of conflicting views of the basic assumptions of the field. Lehrberger/Bourbeau
(1988) represent the traditional view in (2).
(2) The obstacles to translating by means of the computer are primarily linguistic. […] the
computer must be taught to understand the text - a problem in artificial intelligence.
(Lehrberger/Bourbeau 1988: 1, original emphasis)
Landsbergen (1989) expresses himself much more cautiously. He suggests that machines
cannot translate in the sense that translation is understood by a professional translator, but
they can be of help and are able to provide all linguistically possible translations. At the same
time, a group of researchers at IBM had started exploring the use of information theory in
MT. The first published presentation of this project was by Brown et al. (1988). The defiant
attitude of this group is reflected in the probably apocryphal quote from the research director
that "Each time I fire a linguist, the system's performance improves". This attitude is a
reaction to the protracted failure of linguistic theory to live up to the expectations and
constitutes a radical rejection of (2).
Information theory, developed by Shannon (1948), is a branch of mathematics. Applied to
MT, it requires a large parallel corpus, i.e. a number of SL texts with their TL translations.
The first task is to align the SL and TL corpora, i.e. state which word(s) in the SL corpus
correspond to which word(s) in the TL corpus. For large corpora, it is practically impossible
to do this manually and a major part of the effort in this approach to MT is devoted to
automatic alignment procedures. Once the aligned parallel corpus is in place, information
theory provides the formulae to calculate the most likely translation of a word in context on
the basis of the most probable TL correspondence to the SL word and the most probable TL
word in the TL context.
In a sense, the IBM project described by Brown et al. (1988, 1990, 1993) eventually
implemented Weaver's (1955 [1949]) suggestion to use information theory in MT, following
the development of computer technology beyond a certain threshold. This happened at a time
when many scholars in MT were looking for a new way of solving their problems. The impact
of this approach can be measured by the fact that in 1992, the fourth TMI conference
(Theoretical and Methodological Issues in Machine Translation) was entirely devoted to
"Empiricist vs. Rationalist Methods in MT". Here empiricist refers to the use of information
theory and rationalist to the use of linguistic theories.
To many people in the field, the emergence of these empiricist methods seemed like a
revolution. As argued in more detail in ten Hacken (2001a, b), this perception is not correct.
Without repeating the entire argument, let me mention two indications that the emergence of
the use of information theory in MT was less revolutionary than often thought. First, the
evaluation criteria for MT results remained basically the same. In both cases, the percentage

of correctly translated sentences was considered an adequate measure of the performance of
the MT system, so that the results of the two approaches could be compared directly. Second,
it did not take long before a merger between the two approaches could be observed, e.g.
Klavans/Resnik (1996). While Gazdar (1996) calls this a paradigm merger, under a standard
Kuhnian interpretation of paradigm, a merger of paradigms is a contradiction in terms,
because different paradigms are incommensurable, cf. Kuhn (1970).
The influence on the field of CALL of the emergence of approaches based on information
theory is also less than revolutionary. Independently of the question of how successful the
approach was in MT, the problem of CALL is of a significantly different nature. Before the
first public presentation of information theory-based MT, Nirenburg (1987) formulated the
problem of MT as in (3).
(3) The task of MT can be defined very simply: the computer must be able to obtain as input a
text in one language (SL, for source language) and produce as output a text in another
language (TL, for target language), so that the meaning of the TL text is the same as that of
the SL text. (Nirenburg 1987: 2)
The crucial point of (3) is that it allows a view of the task of MT as the mapping between two
corpora. This is the central condition for the successful application of information theory.
CALL cannot be modelled as a mapping of this type in any plausible way. The goal of CALL
in general can be described as contributing to second language acquisition. The starting point
and the end point of second language acquisition are not corpora, but knowledge states in the
learner's mind. Instead of a mapping between an SL text and a TL text, CALL is meant to
improve the learner's knowledge.
From the perspective of CALL, a potentially interesting property distinguishing information
theory from NLU is its robustness, because dealing with non-well-formed input is one of the
problems of CALL. Lack of robustness is a major problem of NLU components. When they
encounter a sentence which cannot be parsed with their grammar, they reject it. It is very
difficult to turn the reason for rejection as perceived by the parser into sensible feedback to
the learner. In practice, in many cases sentences are rejected although native speakers would
accept them. Therefore many CALL applications resorted to pattern matching, searching for
particular types of error. The question is then to what extent the robustness in information
theory can be put to use in CALL.
Information theory always gives the best possible match. In MT, rather than crashing or
stating that the sentence cannot be translated, the system will give the most probable
translation, regardless of how low this probability is. In dealing with ungrammatical input in
CALL, the advantage would be that the system does not crash, but the disadvantage is that
errors are not recognized. While robustness is definitely a great advantage in MT, which deals
with the meaning of its input, it is of doubtful value in CALL, where discovering and
describing errors is one of the aims.
Even if individual tasks in CALL could be modelled as a mapping of the type represented by
(3), the performance of components based on information theory requires a knowledgeable user. While 90% correctness seems a reasonable score, one would not like to expose a learner
to a module which gives the wrong feedback in ten percent of cases.
4 The Revolution in Computational Linguistics
A revolution is a much more far-reaching change of orientation than the replacement of one
approach by another. It involves not only the means by which problems are approached, but
the problems themselves. The rejection of (2), the view that when NLU does not yet work,
more linguistics has to be added, is not necessarily a revolution. An actual revolution occurs
when statements such as (3) are no longer considered valid.
In the 1990s, the insight gained ground that MT as formulated in (3) is not a well-formed
problem, i.e. a problem for which a unified solution is possible. Instrumental in this
development was the Verbmobil project, carried out in Germany from 1994 to 2000. In the
preparation of this project, Kay et al. (1994) argued in detail that translation of the type
implied by (3) has little in common with translation as conceived of by human translators.
Even though they do not refer to modern theories of translation, they insist on the fact that
what is a correct translation of a sentence crucially depends on the linguistic context and the
pragmatic situation.
Although (3) mentions the text as the level of translation, MT prototypes developed in the
1970s and 1980s invariably took the sentence as the basic unit. Much attention was paid to the
treatment of syntactic differences across languages of the type illustrated in (4).
(4) a. All bishops like her.
b. Zij bevalt alle bisschoppen.
The example in (4) is taken from Leermakers/Rous (1986). It is a pair of an English sentence
and its Dutch translation. The Dutch verb bevallen (as well as its equivalents gefallen in
German and plaire in French) is the most natural translation of like, but the two arguments are
reversed. In discussions of the translation part of MT, which gradually gained importance in
the late 1980s, structural divergences such as (4) and lexical divergences, illustrated by the
translation of put into Dutch (zetten when the result is vertical, leggen when it is horizontal),
occupied a central position. Rosetta (1994) and Dorr (1993) present two opposing views, with
some discussion in the reviews by Dorr (1995) and Arnold (1996).
As had become common ground in translation theory in the meantime, e.g. in the skopos
theory of Reiß/Vermeer (1984), the situation of a text supplies essential information for its
translation. Nord (1989) even proposes a detailed analysis of the translation contract as a first
step in the translation process. Given this background, the study of divergences such as (4),
with its deliberately baroque lexical choice, or the Dutch translation of put are at most
relevant in linguistics, not in translation.
If translation is situationally determined, a general solution of (3) is inherently impossible.
Instead, Verbmobil went on to define a particular setting for which it would provide a
solution. This setting, translation of spoken dialogues about fixing details of a meeting
between German, English, and Japanese, seems rather more difficult than text translation. In
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31
fact, however, it was chosen quite cleverly as a problem for which a reasonably successful
solution is possible. The essential point is that, in the type of dialogue chosen, the two sides
are cooperating to achieve a common goal. They are trying to understand each other, which
implies that they accept imperfect input, ask for clarification if necessary, and confirm
intermediate or final results as appropriate. The Verbmobil system, as described by Wahlster
(2000), exploits these features of normal cooperative dialogue to make up for any failure to
produce the best translation of individual dialogue turns.
As argued in ten Hacken (2001a), the transition from an attitude as reflected in Rosetta (1994)
and Dorr (1993) to the approach found in Verbmobil constitutes a revolution. The former
attitude is marked by the adoption of a holistic approach to the field of MT reflected in (3)
and the central concern with linguistic theories and problems. The Verbmobil approach is
marked by a focus on a special problem of practical communication. The choice of
knowledge to be applied is made subordinate to the choice of a genuine, practical problem. As
shown in ten Hacken (2001b), this revolution in the domain of MT is only a special case of
the more general revolution in CL.
One of the implications of the nature of the revolution is that, even if we restrict our attention
to MT, the Verbmobil approach as such cannot and need not be generalized to the entire field.
It is precisely the dissolution of the general problem of MT which constitutes the revolution.
The question is then which aspects of the approach can be generalized. A straightforward
candidate seems to be the use of sublanguage, a subset of language used in a specific
situation. Kay et al. (1994) are adamant, however, that Verbmobil is not based on a
sublanguage. Although the vocabulary required for arranging business meetings was entered,
they emphasize that no architectural decision based on this particular sublanguage should be
admitted in the system. The problem for which Verbmobil was designed is the translation of
cooperative spoken dialogues. Success should not be measured in terms of the percentage of
correctly translated turns, but as a percentage of successfully concluded dialogues.
It is instructive to compare the Verbmobil approach with domain-specific systems developed
in Artificial Intelligence (AI) and with sublanguage systems. Barr/Feigenbaum (1981)
describe a number of AI systems for NLU and MT. Their starting point is not a particular
situational or communicative setting, but a specific conceptual domain. The main problem
they encountered is the delimitation of the domain. This is a serious problem, because only
the domain is modelled in the system so that the system cannot handle input referring to
entities outside the domain. In Verbmobil, it is not the domain but the communicative setting
which is covered. Whereas the conceptual domain of AI systems does not correspond to a
real-life problem, as their poor performance indicates, the communicative setting chosen by
Verbmobil does.
Sublanguage systems are similar to the domain-dependent AI systems, but their domain is defined
by a naturally occurring subset of the language rather than by a conceptual domain. The
use of sublanguage was considered by many to be the only road to success in MT. In their
overview of (pre-revolutionary) MT, Lehrberger/Bourbeau (1988) formulate this as in (5).

(5) the success of FAMT in the immediate future can be expected to be limited to domain
dependent systems. (Lehrberger/Bourbeau 1988: 51)
In (5), "FAMT" stands for Fully Automatic MT and "domain dependent" means taking a
sublanguage as its basis. The main case in point illustrating the success of such systems is
Météo. As described by Chandioux (1976, 1989), Météo is a system for the translation of
weather forecasts from English into French, which has been operational since 1976. Although
the success of Météo was generally attributed to its using a sublanguage and triggered
extensive research into the use of sublanguages in MT, cf. Kittredge/Lehrberger (1982),
Kittredge (1987), it has never been possible to replicate the success. Even sceptics such as
Landsbergen (1989) accept that the source of Météo's success is its use of a sublanguage. He
attributes the success of Météo as opposed to the lack of success with other sublanguages to a
special property of the sublanguage of weather forecasts.
The nature of the revolution in MT suggests a different interpretation. Originally, as described
by Isabelle (1987), TAUM-Météo was developed as a simplification of a more general
approach to MT. The subsequent extension of the same method to the domain of aircraft
maintenance manuals was less successful, although Isabelle suggests that funding was
stopped before a definitive judgement could be passed on the success of the method.
Chandioux (1989) describes how the system as used successfully at the Canadian weather
forecast centre is no longer the stripped down general MT system, but a completely
redesigned new version, specifically geared to the situation at the Canadian translation
bureau. Rather than a successful sublanguage MT system, Météo can therefore be seen as a
forerunner of the approach taking a concrete practical situation as a basis and aiming at
communicative success. This explains why the record of successful sublanguages was
basically restricted to Météo: even the success of Météo should not be attributed to its choice
of a successful sublanguage, but to its accidentally finding a good problem to solve. Not the
sublanguage, but the communicative situation (including a fairly rigid style sheet) was
responsible for its success.
The revolution in MT, of which Verbmobil is perhaps the first explicit reflection, can
therefore be seen to have its forerunners. This is not surprising in view of the general theory
of the nature of such revolutions. Before the Copernican revolution as described by Kuhn
(1957), Aristarchos of Samos claimed that the Earth moved around the Sun rather than the
other way around, cf. Heath (1981 [1913]). A common property of Aristarchos and Météo is
that neither they themselves nor the field at large realized the revolutionary nature of their
achievement. The revolution had to wait until the field had entered a crisis. In the case of MT,
and CL in general, it can be argued that only with the revolution did it turn into a genuinely
applied science, cf. ten Hacken (2001b).
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5 Implications of the Revolution
The story of CL as presented so far seems very much the story of MT. In fact, MT was a very
important part of CL during the first few decades of its history. Starting as the earliest
application in CL, MT was then reanalysed as the most obvious test for NLU. MT was also
central in the revolution in CL. It was here that the clash between expectations and actual
performance led to the deepest sense of crisis. MT is a task which can be explained easily to a
wide audience, so that it has generated extensive general interest, wide publicity, and
substantial funding. When the expectations raised in this way are not fulfilled, such a
recognition will equally get wide coverage.
It is to be expected that the most immediate implications of the revolution will also concern
MT. By choosing dialogue translation, Verbmobil takes a promising subfield of MT. The
project can hardly be blamed for choosing this problem and exploiting its structure, but the
transfer of insights gained from dialogue translation to the translation of texts, to which most
effort had been devoted before the revolution, is not straightforward. It is a useful exercise,
however, to investigate how the revolution affects an established field, modifying its habits of
thinking as studied in the sciences by Margolis (1993).
A post-revolutionary approach to the translation of text does not start from the assumption
that text translation is a unified field. A central question to ask is why we want a particular
text to be translated. Depending on the answer, different fields can be distinguished. These
fields are at the same level of generalization as dialogue translation.
Ten Hacken (2003) distinguishes three types of text translation, each with a different reason
why the text should be translated. The first type has the translated text as an authority, as in
contracts or legal documents. Here support for human translators by means of translation
memories, terminology management, and similar tools as foreshadowed by Kay (1997
[1980]) is the most promising prospect.
The second type is translation in order to know what is in a text. In such cases, translating the
text is a possible, but not the most efficient approach. A summary of the text is in general
more useful than the full version. Text summarization, described by Mani (2001), is a
relatively new field, but it can already claim a number of successes. Depending on the setting,
summarization can be followed by human translation, foreign language reading, or machine
translation, but in any case the problem is much reduced.
The third type of text translation is aimed at finding the answer to a specific question. It can
be a how-to-question ('How can I insert a photo into my web page?') or a question about a
particular fact ('Who is in charge of official translation services in the canton of Fribourg?'). If
we find a foreign-language manual, book, or web-site where this is information is supposed to
be, translation is a particularly inefficient method to find the answer, because most of what
will be translated is not part of the answer. Here, different types of information retrieval can
be considered. From the point of view of the end user, Answer Extraction as described by
Molla et al. (1996) is probably the most efficient approach, but much depends on the concrete
setting. In the same way as for summarization, further steps are necessary for the
interpretation of the result, which is still in a foreign language, but the problem is much
reduced in a fairly literal sense.
The new approach to choosing and analysing problems overcomes the habit of seeing any
problem involving foreign languages as a translation problem to be treated as in (3). A
consequence of the revolution is that for the first time in CL the question as to why we want a
translation is raised. This approach is not specific to MT. Ten Hacken (2001b) argues that it
reflects a more general revolution covering CL as a whole. In fact there is nothing specific to
MT in the move from language-oriented to problem-oriented work.
Let us now return to CALL and consider the implications of the revolution in CL. A
comparison of CALL and MT first of all shows how different the two fields are in the
relevant aspects. Whereas for MT it has long been claimed that, given a set of languages,
there should be a single system solving the problem once and for all, cf. (3) above, such a
claim has always seemed ridiculous for CALL. The 'dissolution' of MT in the revolution in
CL has removed this difference.
In the new approach, the choice and analysis of a particular problem takes a central position.
In the same way as translation can be thought of as a 'framework problem' defining a broad
field, for which MT investigates how computers can best be applied, CALL has second
language acquisition (SLA) as its framework problem. Similarly to translation studies, SLA
also has its practical and theoretical branches. Unlike translation theory, much theoretical
work on SLA is oriented to the use of SLA data in determining the nature of language, e.g.
Hawkins (2001), Flynn/Lust (2002). The major difference on the practical side appears to be
that the result of translation is a text, whereas SLA results in cognitive abilities. In view of the
revolution in CL, the former view has to be adjusted. The text is not in any absolute sense the
optimal result of translation, but only the most common side effect. What we are trying to
achieve with translation is also ultimately cognitive and need not involve a TL text with the
same meaning as the original.
Typical applications in CALL such as vocabulary trainers and authoring tools for cloze tests,
discussed in section 1, are not at the same level of abstraction as the problem of dialogue
translation chosen by Verbmobil. Given a setting in which dialogues of the relevant type
occur, Verbmobil can provide a complete solution. Vocabulary trainers and cloze test
generators do not provide a full solution to a type of SLA. They are dedicated systems for
specific tasks in the domain of SLA, which depending on the theory of SLA and the teaching
strategy chosen can be part of a solution to SLA in a particular setting.
As an example of a CALL system illustrating some of the typical features of the more
comprehensive, problem-oriented approach I will take here the ELDIT system
(http://www.eurac.edu/eldit). This choice is not entirely arbitrary, although it is of course not
the only modern CALL system. For a recent critical overview of intelligent CALL systems,
cf. Gamper/Knapp (2002). ELDIT has been developed at the European Academy in Bolzano
for the specific bilingual situation found in South Tyrol. In this bilingual region (German and
Italian), few people grow up as true bilinguals, but a certain level of bilingualism is required
for people employed in public administration. The ELDIT system originated as an advanced type of electronic learner's dictionary, cf. Abel/Weber (2000). Rather than simply mapping
the structure of a paper learner's dictionary on a screen, the system exploited the additional
possibilities offered by a computer. An example is the presentation of paradigmatic semantic
relationships between words as a diagram with the base word in the middle and labelled
relationships to other words, linked to explanations on semantic and stylistic characteristics
and contrasts between them and the base word. Moreover, the entire learner's dictionary has
been developed in parallel for German and Italian, so that at any point the user can switch
between languages, for instance in order to check the equivalent in the native language.
Importantly, the interface is an essential part of the system. At each point, the needs of a user
with limited experience of computers are considered. Thus straightforward, self-explanatory
visual representations are preferred to the use of a help module.
Two steps in the extension of ELDIT to a full-fledged learning environment are the inclusion
of verb valency and morphology. The system for the encoding of verb valency is described by
Abel (2002). The extensive use of insightful diagrams is intended to make the information
accessible to the linguistically naive user. For the integration of morphology, knowledge from
the Word Manager (WM) system for morphological dictionaries was used. WM databases
include both inflection and word formation. The structure of WM is described in ten
Hacken/Domenig (1996). The specific use of the organization of information in WM for
CALL was recognized by ten Hacken/Tschichold (2001). The use of this information in the
context of ELDIT is described by Knapp et al. (2003). With the integration of WM into
ELDIT, words in a text can be analysed in terms of inflection and word formation and
mapped to their dictionary entry. It is also possible to start from a dictionary entry and obtain
the inflectional paradigm or the word formation relationships of the base word. The latter are
represented in diagrams comparable to the paradigmatic semantic relationships and allow
successive exploration of relationships in the dictionary. Finally, it is possible to inspect the
rules for inflection and word formation, presented in an appropriate way for the chosen group
of users.
Although ELDIT at the moment does not include NLU modules, it definitely uses CL. The
computational modelling of syntactic, semantic, and morphological knowledge related to the
lexicon goes beyond simple listing and pattern matching. The use of a model of (aspects of)
language qualifies it as using CL in CALL.
6 Conclusion
The discussion in this paper started from two questions, one of them directed to the past (why
are CL and CALL traditionally separated?) and one to the future (why are the prospects of
collaboration now better?). The relevant distinction between the past and the future is that a
revolution in the field of CL has thoroughly changed the general approach. Before the
revolution CL concentrated on NLU. CALL is not useful as a test for NLU and NLU
components are at most marginally useful in CALL. After the revolution, CL has turned to the
detailed analysis of practical problems. CALL provides an interesting set of such practical
problems. A revolution does not mean that all earlier knowledge is lost. In fact, researchers try to save as much of it as possible by reinterpreting it in the new framework. Parsing
techniques and theories of grammar are still used, but in a more interesting way than before.
CALL is likely to be among the typical fields of application of CL in the future. In ten
Hacken (2001a) it is argued that the revolution in MT took place between 1988 and 1998. The
post-revolutionary future with its bright prospect of the collaboration of CALL and CL has
already begun.
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Short words, basic ideas

by Carlos Carrion Torres - Vitoria ES - Brazil

When one considers Indo-European languages, especially those which are properly European, one can perceive that there are many similarities among them. These similarities surely are due to their roots in the very distant past of civilized mankind, going back to the early Sanskrit language.

With the exception of a few cases, modern words for things, feelings, beings and thoughts that are very primitive, basic, ancient, well known and easily perceived by mankind are very simple and quite short.

Those words are nowadays short and simple and they were probably also that short since their ancient origin.

Even considering that most of the words in European languages with either simple or sophisticated meanings, are monosyllabic or bisyllabic, there is some pattern that confirms that nouns for well known things are generally shorter.

That perception is easy, even without any further deeper research.
Here are some examples from English:

* mother, mom, father, dad, brother, sister, son, daughter, boy, girl, man, woman, baby, friend;
* hand, foot, eye, ear, heart, leg, arm, face, nose, mouth, body, head, knee, ankle, thigh, neck, hair, beard, tooth, finger, nail;
* cat, dog, horse, bull, ox, cow, lamb, donkey, duck, elk, lion, bear, wolf, pig, hen, goat, frog, toad, bird, fish
* love, hate, god, good, mad, evil, well, death, here, there, under, over, back, front;
* sky, sea, lake, river, sun, moon, rock, cave, tree, sand, stone, water, cloud, rain, snow, night, day, dawn, year, fruit, apple, lemon, grape, lunch, food;
* Yes, No, tall, short, long, hot, cold, warm, poor, rich;

Also, frequently-used verbs with simple, ancient meanings, which express feelings, behaviors and actions are usually short.

Almost all of these verbs are also very irregular, for example auxiliary and modal verbs;

Irregularity is quite obvious, because those verbs can be considered as "natural" ones. Rules to standardize languages came only later, when talking was systematized by people.
Some examples from English

* meet, come, go, eat, drink, sleep, wake, talk, walk, run, dive, swim, die, do, make, feel, work, give;
* want, must, shall, will, can;
* be, have, do;

Why are such word short? This seems quite obvious: first things comes first. Primary languages first used most of possibilities of monosyllabic or bisyllabic sounds. When most sophisticated and complicated subjects required new words, there were few possibilities left for monosyllabic or bisyllabic words. So longer words were necessary.

Another easily realized language pattern, that has also obvious roots, is that there are more differences among words with the same meaning in different languages for ancient and traditional stuff, than for very recent, modern, technological, medical, nouns.

Rhotics Ready: Getting a Handle on the Consonant "r"

by Jime Palacios

Cross section of the human head showing the parts involved with pronunciation

Rhotics - sounds that are produced when the character "r" is written - are some of the most difficult sounds to learn when studying a second language. These sounds are the trills, the central approximants, the taps, the flaps, and the fricatives of phonetics. If this list of phrases seems a bit daunting, take comfort: language acquisition may be a slow process but it is something that can be incredibly fruitful. After all, language is powerful. It is the sword of Shakespeare, a way for Einstein to tell us that E=mc2, and the means through which we express love.

What makes rhotics so difficult to learn is its variation across languages. The sound "r" can be produced in Spanish as a rapid tapping of the tip of tongue on the roof of the mouth, while in English an "r" is produced by a prolonged, smooth current of air that glides over the centre of the tongue. And, in most Asian languages, such as Japanese or Korean, the use of "r" is not distinguished from its lateral approximant brother, "l".

It is important to become aware of these subtle nuances when beginning the study of a second language because pronunciation is a key part of learning a language. Fortunately, for those learning English, there is only one rhotic sound. Those of you seeking to study Toda, however, will have to learn three different rhotic trills.

There are several ways to practice and learn pronunciation, ranging from immersion, through television, radio programs and travel, to taking a formal course in your second language. Taking a course is relatively simple in England as English language schools are located across the country, allowing you to take an English course in London or elsewhere.

The most tried and true advice for language learning focuses on practice. Find yourself a language partner who will encourage you to use the language skills you have already acquired and assist you in correcting the ones you have yet to master. English language schools (more info) tend to have formal and informal opportunities to do this exact thing.

However you go about reconciling your "r" with the new one in your second or third language, don't forget how incredible it is to be able to communicate with an entirely new group of people. Right now there is a whole repository of reading and recitation material just waiting for you to round this language corner. And if English is the second language you are learning, mastering the "r" means you'll be able to say that last sentence with the fluency of a native speaker!
References

Ball, Martin J. and Joan Rahilly. Phonetics: The Science of Speech. Arnold: London, 1999.

Catford, J.C. A Practical Introduction to Phonetics. Oxford University Press: Oxford, 2001.

Ladefoged, Peter. Vowels and Consonants. Blackwell Publishing Inc: Oxford, 2006.

Ladefoged, Peter and Ian Maddieson. The Sounds of the World's Languages. Blackwell Publishers Inc.: Oxford, 1996

New Perspectives on Digraphia

by Elena Berlanda
Abstract

This paper seeks to expand the notion of digraphia, a term which is often used to describe a situation where a language is written with two different scripts. This expansion of the concept shall be reached by taking a systematic look at the way language and scripts relate to each other.

Writing systems change, digraphia and orthography reforms have not been treated extensively in the literature published on scripts and writing systems. Furthermore, an all-encompassing view on the script-language relationships has not yet been undertaken.

In the field of linguistics, writing systems are generally not considered to be topics that have the same importance as other language contact phenomena such as bilinguialism & diglossia, language shift, language death, language attrition, language convergence and others. Inquiries about these phenomena are generally explored by taking into account the connection between the languages and the communities using them. While the choices societies make about languages are well documented, as well as being supported by sufficient theoretical foundations, this cannot be said to be the case of the relationship between scripts and socities.

This paper explores the question of a script-community relationship from a theoretical perspective with the goal in mind to lay the theoretical grounding for a serious inquiry into the choices made about writing systems. The framework presented here lists a number of possible choices which a community can take about writing and literacy in general. As it will become immediately clear this decision-making involves far more than simply choosing a script. It is argued that not only scripts are meaningful, as they acquire meaning through being used. Instead, a view is presented here which regards the kind of option a society chooses for its literacy as equally meaningful, since choices made about literacy can express the tensions within a community. This can give insights into the political and socio-cultural shifts and changes of a speech community and it can also be a way of indexing various aspects of identity.

Read the paper (PDF, 17MB)
About the author

Elena Berlanda is originally from Austria but currently lives in Canada. She has just completed a Master's degree in linguistics at York University (Toronto, Canada) and wrote this major research paper for her MA.

Spaced repetition learning systems (SRS)

by Michael Mounteney

Computer-based spaced-repetition, or flashcard, software, can greatly assist in the rote-memorisation of information that is arranged in question-answer pairs; in the case of language study, the obvious application is memorisation of vocabulary.

The method varies in detail from one application to another but basic modus operandi is thus:

1. Each day, run the software.
2. It will present a question, typically the translation of a word or phrase. The translation can be active or passive (into, or from, the foreign language).
3. You attempt to recall the answer from memory.
4. Having made your best attempt to do so, you click a button to reveal the correct answer. You then score your answer by clicking a further button. Some systems such as FullRecall (http://fullrecall.com) and Supermemo (http://www.supermemo.com) allow you score from 0 (no idea) to 5 (instant and correct recall), while others such as jMemorize (http://jmemorize.org) only accept right/wrong.
Some systems (e.g., Parley on KDE) make you type the answer and score what you type. This prevents unconscious cheating but prevents discretion in scoring, such as omission of a leading `the'.
5. The software schedules the question to be asked again at some point in the future which it calculates from your scoring history to date, i.e., how well you answered this time plus how well you have answered the same question in the past. Different packages use different algorithms for calculating the scheduling.
6. The above steps are repeated until all items scheduled for today have been reviewed or answered. Therefore, the items that are presented on any particular day are those items that have been scheduled on previous days' reviews.
7. Items that you remember easily are scored more highly and are repeated less and less frequently, whereas items that are more difficult are repeated more frequently - hopefully until they are drilled-in and remembered. This is the key to spaced-repetition software: it automatically manages revision of your learning, so that you concentrate on items that are difficult to remember, and don't waste time reviewing items that you know very well.
8. The obvious chicken-and-egg question is how items are started in the first place. Most systems have the idea of a queue of unlearnt items, that are in the database but have not yet entered the learning process. Generally, on any day, you would review the accumulated items from previous revisions, as above, then add some more items from the queue. Thus each day ones database of `learnt' items grows slightly.

Different packages have varying levels of sophistication and facilities.

Supermemo excels at statistical analysis of learning, and provides comprehensive management of items into categories and sub-categories. It is however rather idiosyncratic in its operation and its text handling is not simple Unicode, so learning data in non-Latin writing systems is sometimes inconvenient. Supermemo is moving towards another idea called `incremental reading' which involves harvesting pages of information off the Web and breaking it up into memorable pieces over time. It only runs under MS-Windows, although some users have had some success running it under WINE on GNU/Linux. It also offers comprehensive multimedia capabilities. Its database is proprietary and any editing thereof has to be through the program's own facilities, which are less than comprehensive.

FullRecall is much more straightforward in operation. It implements Unicode (UTF-8) character handling, so all writing systems are accommodated. Its spacing algorithm is intriguing - a `neural net' which supposedly mimics your personal `forgetting curve' (the rate at which your memory decays over time) based on your self-scoring during reviews. It is multi-platform. It is still in development, but that does not mean that it is unreliable; the development is of new features. It achieves portability by the use of the FOX toolkit, which, in the opinion of this author at least, is a rather simplistic and unaesthetic product. Its database is open, which allows some analysis and reporting by third-party tools.

jMemorize is a free (donations welcome) package written in Java. It implements the rather simple Leitner spacing algorithm, less sophisticated than either FullRecall or Supermemo. As it is written in Java, it runs on any machine which has a JRE - MacOS, GNU/Linux, Solaris, MS-Windows XP etc.

Mnemosyne is similar to SuperMemo and uses an early version of the SuperMemo algorithm, with some modifications that deal with early and late repetitions. It is written in Python and can be used on Windows, Linux, and Mac OS X. Users of the software usually make their own database of cards, although some pre-made databases are available.

Each day the software displays each card that is scheduled for repetition. The user then grades their recollection of the card's answer on a scale of 0-5. The software then schedules the next repetition of the card in accordance with the user's rating of that particular card and the database of cards as a whole. This produces an active, rather than passive, review process.

Language101.com is an online spaced repetition program with pre-loaded lessons in Spanish, French, Russian and German. It uses an algorithm similar to one cited in a 1967 paper by Paul Pimsleur. Firefox is the recommended browser.

The user sees a literal translation and a good translation of the phrase being learned. There is also a blank line with one underscore for every letter of the foreign language phrase. The user can click a button to hear the phrase said very slowly, or a different button that will play the phrase at normal speed and display the text.

The user then grades their recollection on a five button scale that ranges from "Beginner or Totally Forgot" to "Right I Know This Well."
Links

Further information about spaced repetition
http://en.wikipedia.org/wiki/Spaced_repetition
http://www.memorati.com/articles/what-is-spaced-repetition.html

Spaced repetition systems
http://fullrecall.com
http://www.supermemo.com
http://jmemorize.org
http://ichi2.net/anki/
http://www.digitalmeadow.com
http://www.flashcardexchange.com
http://www.mnemosyne-proj.org
http://language101.com

Reviews of SRS
http://www.cunning-linguist.co.uk/blog/review-anki-spaced-repetition-systems-srs.html
http://www.gbarto.com/multilingua/confessions/2008/04/spaced-repetition-systems.html

First language attrition

by Céline Graciet

Most people take their mother tongue for granted. They don't consider the fact that those who live in a foreign environment are at risk of losing some of their language skills and fluency if they aren't looked after. First language attrition is a well-known phenomenon that has been widely studied and as a French woman living in the UK, is something that I am acutely aware of. The fact that I am also a translator means that I have a professional interest in maintaining my mother tongue as if I didn't, the quality of my work would suffer. So, as a paranoid French translator living in an English-speaking country, here are some of my thoughts and experiences of acquisition of a foreign language while trying to maintain one's mother tongue.

From the moment I set foot in England, I was determined to make the most of my nine months studying at the University of Sussex; I decided to avoid French speakers at all cost and to do everything I could to socialise with English speakers. This meant three months of initial misery, as I resisted joining the French community which rapidly formed on campus, but didn't master colloquial English well enough to form meaningful relationships with English speakers. However, once my English improved, which happened quicker than if I had mixed with French speakers, I made friends with a few natives (some of whom I still see regularly), and as a result of this early strategy, I don't actually know any French speakers in Brighton. This was great at first, when I needed to concentrate on my second language, but not so great now that I do need to practice my French.

Indeed, language is maintained through a continuous process of repetition and imitation and I have noticed that, if I'm not careful, my French weakens and English tends to pollute it, in several ways:

* Language mixing: an English word replacing a French one
* Syntactic interference: use of English structures which wouldn't necessarily be used in French (for example, the passive voice, which is more common in English than in French)
* General ability to pronounce words correctly (both languages seem to use different muscles, and my French-speaking muscles can get a bit out of shape)
* Literal translation of an English expression into French
* Static vocabulary: missing out on the evolution of French
* Cultural references: when referring to shared codes or experiences, the use of English referents in French

I remember reading somewhere that expatriates tend to retain their first language better, unconsciously "clinging on" to it, if they see their cultural make-up as central to their identity. I don't tend to define myself by my country and culture of origin, and blend happily into English society; I don't see the loss of a certain "Frenchness" (whatever that is) as a threat to my identity and my sense of who I am, and I think that if I didn't love languages as much as I do, I would probably let my French deteriorate to the point where I wouldn't sound like a native speaker any more.

Fortunately (and paradoxically), it is getting easier to retain my French. The Internet has made it very easy to access French newspapers, magazines, radio programmes, films, etc., and I spend on average an hour and a half listening to French. I try and vary my sources, so I stay familiar with its various different levels/registers: political debates, news programs, comedy, films... I even tried to lurk in teenage chatrooms to keep up with slang, but couldn't cope with "text message spelling" for very long. I don't just listen to French: Skype also means that I talk to my mother as often as I like, almost as easily as if she was in the next room (provided she's not chatting in her shop), which gives me direct practice. I recently participated in NaNoWriMo (National Novel Writing Month), and although I ran out of steam and ideas after 21,000 words or so, writing in French was very useful, and I'm planning on taking a long-distance course to carry on honing my writing skills. Practice is key!
About the author:

Céline Graciet is an English to French translator and English to French and French to English interpreter from France who lives in Brighton, England. Her website can be found at: http://www.nakedtranslations.com and she runs a bilingual blog about translating, interpreting and language at: http://www.nakedtranslations.com/en/blog.php

SLANG - Are you In or Out?

by Amy Newsome

Traditional interest in the variety of language called 'slang' and the usage of this variety has been highly prescriptive, something that budding linguists are always told not to be. Hence, a different approach is needed, namely a more descriptive one that relates to ideas and concepts relating to the field of study called Pragmatics.

A concise definition of the term 'slang' is: a dynamic variety of language that is used to show solidarity and claim in-group membership, and as suggested by Gibbs (1994). Slang is also one of the most important 'mechanisms' or devices for showing social awareness. This variety of language often occurs around 'taboo' subjects such as sex, drugs, alcohol, homosexuality, etc.

According to Lodge (1997), the colloquial or vernacular use of language is extremely important, not only to sociolinguists, but in the study of semantics and meaning in context. In his study, Lodge identifies three features of language variation that he believes are essentially true:

1. Variability is natural in language and essential to its social role in our everyday lives. A broad and increasing lexicon is essential in order to express the 'nuances' of human emotions and personal identity and experiences.
2. There are no 'breaks' in language varieties, meaning that there are no pure homogeneous styles and dialects that exist. Rather, there are scales or gradations of linguistic style and language, and these scales are fluid and are subject to change.
3. Language variation is not a free or unrestricted process. Even slang is subject to factors that are outside language, such as age, gender, and cultural background.

In-groups and out-groups

An element that is vital to the usage of slang is notion of in-groups and out-groups. One of the reasons that slang develops is the need for group solidarity (Cutting, 2001). This means that a certain group of people, for example a bunch of moody teenagers, feel the need to alienate their parents and use language that the older generation will not understand. In this case, the in-group would be the teenagers, and the out-group is the parents. The in-group has 'shared knowledge' (Cutting, 2001), and amongst the members they all possess the in-group vocabulary.

In addition, and on a slightly more technical note, there is interesting concept of the Standard Pragmatic Model. This model proposes that people would experience difficulty in understanding and interpreting slang, when compared to the literal meanings of the same expressions. The pragmatic model suggests that meaning should not be hidden or obscure, as all language should follow rules like Grice's Maxims. However, the reason why slang does not follow this model is since in attempting to understand slang on a literal level, it would cause many problems for someone trying to interpret such an utterance. In trying to interpret the factual or accurate denotation, the meaning would be completely irrelevant and nonsensical. This is why the listener is aware that there is a hidden meaning, or connotation behind an utterance.
Knowing when to use Slang

This relates to a concept defined by Gibbs (1994), who suggests that knowing what kind of slang is appropriate in a particular situation is incredibly important. For example, knowing when to use ''inebriated, drunk, wasted or plastered'' when referring to consuming lots of alcohol is what identifies in-and out-groups. Or it's just whether you're cool or not.
Slang's Relation to Grice's Maxims

Now for an example to illustrate how slang can violate the Grice's Maxims of Relevance, Manner, Quantity and Quality.

1 ''I hope our cricketers will crush those touring pansies.''

In this example, there is evidence that some in-group shared knowledge is needed. In this case, it would be understood by a student who knows about the upcoming cricket match (the game, not the insects), but perhaps would not be understood to an older generation or an outsider to this kind of language variety. Here, the Maxim of Quality is violated, as the 'touring pansies' are in fact the opposing cricket team, and not a literal bed of flowers, so the utterance is not entirely true. The Relevance of this example could also be questioned, as it is a fairly obscure sentence that would not be understood by an out-group. The Maxim of Manner is also flouted, as the utterance is ambiguous, and the meaning is not clear. In addition, there is an excess number of words used, violating the Maxim of Quantity.
Conclusion

Therefore, slang is definitely a language variety that can be studied and observed with regard to Pragmatics and related topics. As a final thought, here are two quotes relating to two completely different opinions about the use of this particular variety:

"Slang is the poetry of everyday life and it vividly expresses people's feelings about life, and about the things they encounter" (Hayakawa, 1941)

Or

"The use of slang is at once a sign and a cause of mental atrophy" (Partridge, 1935)

You decide.


References

Cutting, J. 2001. The speech acts of the in-group, Journal of Pragmatics. Elsevier Science B.V.

Gibbs, R. The Poetics of Mind: Figurative Thought, Language, and Understanding. Cambridge, England: Cambridge University Press, 1994.

Lodge, A. 1997. The Pragmatics of Slang. University of St Andrews. Available online: http://wjmll.ncl.ac.uk/issue02/lodge.htm#3
About the author

Amy Newsome is an English and Linguistics student at Rhodes University in South Africa.

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