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394 Machine Translation: Interlingual Methods Philpot A, Fleischman M & Hovy E H (2003). ‘Semiautomatic construction of a general purpose ontology.’ In Proceedings of the International Lisp Conference. New York. Reichenbach H (1947). Elements of symbolic logic. London: Collier Macmillan. Schank R C & Abelson R P (1977). Scripts, plans, goals, and understanding: an enquiry into human knowledge structures. Hillsdale, NJ: Lawrence Erlbaum. Schultz T, Alexander D, Black A W, Peterson K, Suebvisai S & Waibel A (2004). ‘A Thai speech translation system for medical dialogs.’ In Proceedings of the Conference on Human Language Technologies (HLT-NAACL). Boston, MA. Companion Volume 34–35. Vauquois B (1968). ‘A survey of formal grammars and algorithms for recognition and transformation in machine translation.’ In Proceedings of the IFIP Congress-6. 254–260. Whitelock P (1989). ‘Why transfer and interlingua approaches to MT: are both wrong: a position paper.’ In Proceedings of the MT Workshop: Into the 90’s. Manchester, England.

Relevant Websites http://www.cicc.or.jp – CICC website. http://nespole.itc.it – NESPOLE! website.

http://www.umiacs.umd.edu – UMIACS website. http://www.isi.edu. http://www.lti.cs.cmu.edu. http://blombos.isi.edu – DINO browser. http://www-2.cs.cmu.edu – Enthusiast and Speechalator. http://www.ll.mit.edu – CCLINC. http://isl.ira.uka.de – FAME. http://www.cogsci.princeton.edu – WordNet. http://www.globalwordnet.org – Global WordNet Association. http://www.illc.uva.nl – EuroWordNet. http://www.sfs.nphil.uni-tuebingen.de – GermaNet. http://www.ceid.upatras.gr – BalkaNet. http://www.keenage.comChinese HowNet. http://www.gittens.nl – Mimida multilingual semantic network. http://www.icsi.berkeley.edu – FrameNet project. http://www.coli.uni-sb.de – SALSA project. http://www.nak.ics.keio.ac.jp – FrameNet project for Japanese. http://gemini.uab.es – FrameNet project for Spanish. http://www.cis.upenn.edu – PropBank project. http://www.cis.upenn.edu – VerbNet. http://www.cis.upenn.edu – combination of VerbNet and FrameNet. http://nlp.cs.nyu.edu – The NomBank Project. http://aitc.aitcnet.org – IAMTC project.

Machine-Aided Translation: Methods E Macklovitch, University of Montreal, Montreal, Quebec, Canada ß 2006 Elsevier Ltd. All rights reserved.

Introduction The focus of this article is on machine-aided translation (or MAT), with heavy stress on the word aided, and we shall begin by distinguishing MAT from machine translation (or MT) pure and simple. Both, of course, seek to automate the translation process through the use of computers, and in both humans generally have an important role to play. In MT, however, the initiative in the translation process is given over to the machine, and the aim is to automate this process completely, eliminating the human’s contribution as far as possible. In MAT, on the other hand, the initiative in the translation process remains with the human translator, and the role of the machine is to assist the translator in performing certain tasks – normally, those that can be automated with a good degree of confidence and reliability. The fact that MT systems often do not succeed in

automatically producing a high-quality translation – where high quality is indeed a requirement – and that a human must subsequently intervene to postedit or otherwise improve the machine’s raw output should not cause us to lose sight of the fundamental distinction between MT and MAT. Whereas MT ultimately seeks to dispense with the human translator, MAT proceeds from the double recognition that, for highquality translation at least, the contribution of a human translator is generally indispensable, and furthermore, that this situation is not likely to change for the foreseeable future. Why is this? Quite simply because high-quality translation routinely requires a level of understanding that extends well beyond the literal wording of a source text to encompass unpredictable amounts of real-world knowledge, as well as the capacity to reason over that knowledge. Despite the undeniable progress recently achieved by the new empirical methods in machine translation, such knowledge and reasoning capabilities, remain by and large, beyond the ken of today’s computers. Hence, where high quality is a sine qua non (and not just information scanning,

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or gisting, as it is sometimes called), there will continue to be a need for a human in the translation process, in order to compensate for the machine’s limited understanding. Before we go any further, a brief terminological digression. For the purposes of this article, machineaided or machine-assisted translation, computer-aided or computer-assisted translation (CAT) are all taken to be synonymous. Another synonymous variant is machine-aided human translation (MAHT), which is often contrasted with human-aided machine translation (or HAMT), the latter referring to the manner in which humans are called on to pre-edit source texts or postedit the output of fully automatic MT systems.

A Little History It is one thing to establish that humans are not about to be evicted from the translation process, at least not for the foreseeable future. But then what exactly will the human’s role be in this process? Or, put another way, what is the optimal division of labor between man and machine in the translation process? Yehoshua Bar-Hillel, who was the first full-time MT researcher, was also the first to demonstrate the theoretical infeasibility of fully automatic, high-quality translation – or FAHQT, as he called it – based on his famous ‘‘box in the pen’’ example. (See Bar-Hillel, 1960, particularly Appendix III, see also Machine Translation: Overview; Bar-Hillel, Yehoshua (1915– 1975).) Regarding the optimal division of labor alluded to earlier, Bar-Hillel felt that it would be best for the human to intervene either before or after the translation operation proper, but not during it. He recognized, however, that this was an entirely empirical question, the answer to which could change as computers became more powerful and our understanding of natural language evolved. In any case, the arguments that Bar-Hillel repeatedly advanced in the late 1950s and early 1960s largely fell on deaf ears. For the great majority of researchers working on the problem of translation automation in those years, FAHQT remained the only goal worth pursuing, and one that they were convinced could provide a practical solution to the growing demand for high-quality translation. Bar-Hillel had no problem with MT as a legitimate research goal; it was the second contention that he found wholly unrealistic. However, it was not until 1966, when the American government published its (in)famous ALPAC report, that MT researchers could finally be convinced that their projects would not help respond to the burgeoning worldwide demand for translation, and then only because government

funding for MT research all but dried up (see also Machine Translation: History). Insofar as machine-aided translation is concerned, it is probably true to say that it did not begin to attract researchers’ attention until 1980, when Martin Kay published his seminal paper ‘On the proper place of men and machines in language translation.’ (See also Kay, Martin (b. 1935).) In it, Kay refurbished Bar-Hillel’s original argument, to the effect that fully automatic machine translation, although providing an invaluable research matrix within which to study the workings of human language, had very little to offer in the way of practical solutions to the urgent and growing demands being placed on the overtaxed corps of professional translators. The reason, in Kay’s view, was quite simple: we cannot successfully automate what we do not fully understand. The designers and developers of fully automatic MT systems were attempting to mechanize an essentially linguistic operation (translation) at a time when the science of linguistics had not yet provided an adequate explanation of how language works. As a concrete alternative to fully automatic MT – and this was Kay’s truly original contribution – he advocated machine-aided human translation, or more precisely, a device he called the translator’s amanuensis, now more commonly referred to as a translator’s workstation. At the core of his workstation was a sophisticated text editor, complete with a mouse and a split-screen display, one pane being for the source text and the other for the target. (Remember that Kay was advancing this proposal in 1980, before the appearance of personal computers!) Indeed, Kay argued that a well-designed text editor is probably the single most important tool that translators can be provided with; it is certainly the workstation component they will use most intensively. In the next section of his paper, which is entitled ‘Translation Aids,’ Kay went on to propose a number of ancillary programs specifically designed for professional translators that could be grafted onto this text editor. For example: a shared bilingual lexicon to which users can add various levels of information; a source-text analysis program that flags terms or expressions that occur with higher than normal frequency; and a keyword-in-context program; a document retrieval program that would allow the translator to locate past texts that contain material similar to his/her current text. Many of these suggestions have since been embodied in commercial products, as we will show later. But even more important than the specific details of Kay’s amanuensis (at least for our purposes) is the general philosophy of his incremental approach to MAHT. ‘‘I want to

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advocate a view of the problem in which machines are gradually, almost imperceptibly, allowed to take over certain functions in the overall translation process. First, they will take over functions not essentially related to translation. Then, little by little, they will approach translation itself. The keynote will be modesty. At each stage, we will only do what we know we can do reliably. Little steps for little feet’’ (Kay, 1980: 226). Before we begin to catalogue the various types of translation-support tools that have emerged since Kay first advanced his MAHT program, we should mention another historical antecedent, in the form of the large, dedicated terminology banks that first appeared in the early 1970s, such as Eurodicautom, which was launched by the European Commission in 1973, and Termium, which the Canadian government inaugurated in 1975. In both cases, the goal was to help standardize the technical terminology and official appellations used in large public administrations that were officially bilingual or, in the case of the European Commission, multilingual. In both cases as well, the professional translators in the employ of the EC or the Canadian Translation Bureau were among the first users of these computerized databases, and their particular needs had been carefully considered during the design and development phases. It therefore seems legitimate to consider these term banks as being among the earliest translation support tools. Since the mid-1970s, both Eurodicautom and Termium have undergone numerous changes, one of the most important being that they are now accessible to users outside their host organizations. And, of course, many other term banks have since appeared, both in the public and private sectors. Among the characteristics that users most appreciate about these term banks, one is their sheer volume. Both Eurodicautom and Termium, for example, contain well over a million records, and hence many millions of terms. Another is their reliability, as their records are normally created by bona fide terminologists, following well-defined and rigorous terminological practices. At the same time, this last characteristic represents something of a limitation for working translators, because they are not usually allowed to modify or contribute to the contents of these large, centralized repositories. Moreover, the normalization and standardization of new terminology is a slow, painstaking process and not always carried to a successful conclusion. Hence, in the real world of commercial translation, it often happens that for the same concept, different clients will insist on their own specific terminology. And finally, although these term banks may be enormous, they simply cannot cover all

domains with the same degree of detail, particularly in those high-tech domains where progress is now so rapid. Consequently, translators have a need for other kinds of terminological support. They need a means of recording, storing, and retrieving the results of their own terminological research and particular observations; a more flexible tool that would complement rather than rival the large centralized repositories. This is why for many years, even after the appearance of Eurodicautom and Termium, translators continued to record their terminological observations on file cards, which were sometimes printed up as domain-specific glossaries. It was only with the advent of the personal computer in the early 1980s, that it became possible to envisage automating these manually produced glossaries in ways that would be affordable, while at the same time increasing their utility and efficiency for working translators.

Terminology Management Programs Finding the correct lexical equivalent to sourcelanguage (SL) words is clearly a necessary (though obviously an insufficient) condition for translation. Hence, it is not altogether surprising that terminology management programs, intended to meet the needs mentioned in the previous paragraph, were among the earliest specialized translation aids to appear for the new generation of personal computers. The first such commercial application was probably Mercury/ Termex (MTX), developed under the direction of Alan Melby of Brigham Young University. MTX was a memory-resident program that ran invisibly in the background until the translator called it to the screen by hitting a hot-key combination from within the word processor. When the MTX pop-up window appeared, the translator would then type in the term to look up, and the program would display the corresponding record, if one was present in its database. Using various hot-key combinations, the translator could also insert a target-language (TL) equivalent directly into a word-processing document and, of course, edit or add new records to the terminology database. Other administrative routines were also available for sharing personal glossaries by importing, exporting, and merging term data files. There are now a plethora of terminology management programs that offer essentially the same basic functions as MTX, some as stand-alone products, others as components of larger translator workstations (see following). What are the characteristics that translators appreciate most about such programs? One obvious benefit, common to all computer-based lexical resources, is the fact that

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they allow for much easier and more flexible look-up than any printed volume or collection of file cards. Translators do not have to get up from their desk to query a term; in fact, they do not even have to remove their hands from the keyboard. Moreover, most term management programs now allow for queries, not just on the headword, but also on the content of other fields. Suppose, for example, that the user cannot remember a certain headword but knows that some other word appears elsewhere in the body of the entry. It is extremely unlikely that she would be able to find the entry in question in a bound dictionary, although this would pose no problem for a term management program that indexed the content of all its fields. And the same applies, a fortiori, to systems that permit searches with wildcard characters and variable word order. Moreover, most such systems allow the user to specify multiple search criteria, so that they can retrieve, for example, only those terms that belong to a particular domain, on records that were created after a given date, or by a particular person. In short, these term management programs bring to the domain of terminology many of the benefits of other types of computerized database management systems. Another major advantage of these systems, which was only fully realized when individual PCs were linked up in a local area network, is the ease with which they allow individual users to share the results of their terminological research. Once their machines are linked, it becomes possible for one user to immediately have access to new or modified records entered in the database by another user. Of course, this raises obvious problems of database management, such as how to ensure the integrity and the coherence of the database; but none of these problems are insurmountable, and users have generally found that the advantages of sharing their terminological resources far outweigh the costs. (We will return to consider other, more advanced features of recent term management programs later.)

Other Lexical Aids The term management programs described in the previous section are primarily intended for a translator’s personal terminology, i.e., as a repository for the equivalents encountered or researched in the course of personal work. In addition to these programs, and to the bona fide term banks such as Termium and Eurodicautom, other lexical resources also form part of a translator’s basic tool kit, most notably standard bilingual dictionaries. Needless to say, these too stand to benefit from the increased power and flexibility that are afforded by electronic databases.

Interestingly, one of the first to realize this was Alan Melby, who also designed the first personal term management program. In the 1990s, Melby’s company obtained the rights from several well-known publishers to include the contents of certain of their bilingual dictionaries within the MTX product offering. As a result, MTX was able to provide its users with two levels of lexical assistance: when a term was queried that was not in the user’s personal glossary, the system would display the corresponding entry for that term in one of its standard bilingual dictionaries, albeit in MTX format. Somewhat surprisingly, it took the publishers of the best-known bilingual dictionaries some years to respond to the PC revolution and to release their own versions of their standard and still invaluable reference works. Translators also make use of other monolingual lexical resources, including dictionaries, spelling checkers, and even verb conjugation programs. Although these are undeniably useful, their usefulness is certainly not limited to translators. This is why we will have little to say about them here, preferring instead to concentrate on those tools that are designed specifically for translators and that support them in the central and inherently bilingual aspect of their work. In our discussion of the terminology management programs earlier, it was tacitly assumed that it is up to the translator to identify the terms that should be added to the system’s database. In the early 1990s, some researchers (notably Justeson and Katz, 1993) proposed some surprisingly simple techniques that would allow for the automatic identification of candidate terms in a given text. The techniques involved were part-of-speech tagging (see Part-of-Speech Tagging), followed by the extraction of those word sequences in the tagged text that correspond to wellestablished term patterns (e.g., adjective–noun–noun in English), and finally the merging and sorting of the results in order of descending frequency. This algorithm was said to work best on long technical texts, and then only for multiword terms; furthermore, the candidate terms always had to be vetted by a human translator or terminologist. Nevertheless, the results obtained, particularly at the top of the list, very often corresponded to legitimate terms and would therefore be of value to translators who wanted to research their terminology before beginning their translation. But again, these early term extraction programs were monolingual and left the job of locating the TL equivalents of the proposed SL terms to the human translator or terminologist. It was not long, however, before researchers moved to correct this deficiency. In 1994, Ido Dagan and Ken Church published a paper that provided the logical

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extension to monolingual term extraction, in the form of a program (called Termight) that operated on parallel texts (i.e., texts that are mutual translations) in order to identify candidate terms along with a proposed translation. Actually, Church had been one of the pioneers in the development of automatic alignment algorithms that serve to create large bitextual corpora (i.e., parallel texts in which the translational correspondences are rendered formally explicit). In Termight, a word-level alignment program was used to calculate the proposed target language equivalents of the candidate terms, and according to the authors, the system was deployed with success at the AT&T Translation Services, leading to marked productivity increases in bilingual glossary construction. In recent years, there has been much activity in this area of automated term extraction. Monolingual term extraction programs are now available for a wide range of languages, and there are even some commercial products that, like Termight, propose translations for the candidate terms, e.g., TermFinder from Xerox (now marketed by Temis) and, more recently, Term Extract from Trados.

Repetitions Processing The basic idea behind repetitions processing in the context of translation is as simple as it is appealing: a translator should never have to retranslate a given SL segment if an acceptable translation has already been provided for that segment. The job of the repetitions processing program is to determine which segments in a new text have already been translated and then to provide the translator with easy access to the previous translations of those repeated segments. This is usually done in the following manner: The repetitions processor first segments a new text to be translated into basic units; these are generally sentences, but may also include headings, list elements, or the contents of each cell in a table. It then takes each source unit in turn and conducts a search for it, as a simple character string, in a reference file (or bitextual database) made up of source and target language translation pairs. If a repetition of a source unit is found, the associated target segment is retrieved from the database and shown to the translator, who may or may not decide to incorporate it into his/her translation. (Alternatively, if the repetitions processor is operating in batch mode, the translation will simply be inserted into the provisional target text, or the source sentence may be flagged in some way so that it need not be retranslated.) This somewhat simplified description raises a number of important questions. The first concerns the

nature of the repeated segments: Are they always full sentences, or other complete textual units like headings or list elements? And the second concerns the nature of the bitextual database in which the processor searches for repetitions: How exactly is it constituted? As it turns out, the two questions are interrelated. The default processing unit in almost all commercial repetitions processing systems – which are commonly called translation memories (TM) – is the complete sentence, because this is the unit for which it is easiest to build up large-scale databases of past translations; and the usefulness of this kind of tool is obviously correlated with the size of its databases (see also Translation Memories). Most such systems operate interactively, as follows: If no match is found for a given sentence in the text being translated, the translator must furnish the target language equivalent for it, as would normally be done. When that translation is completed and the translator moves on to the next sentence in the text, the repetitions processor links and stores the previous source- and target-language pair in its current database, so that if the source sentence is repeated later in the text, the system will be able to retrieve the associated translation. Now, suppose that later in the text, a source sentence is encountered that repeats only part of the SL-TL pair that has just been stored; say, its verb phrase, i.e., the auxiliary, the main verb and the noun phrase that serves as its direct object. The repetitions processor might be able to locate the repeated source sequence in its database, although even this is far from obvious. (How does one know which subsentential sequence to search for without searching for them all, thereby opening the door to a combinatorial explosion?) But even if this problem could be resolved, how would the repetitions processor know which subsegment to retrieve from the associated target sentence? Some sort of linguistic analysis would have to be performed in order to determine just what part of that TL sentence corresponds to the translation of the SL verb phrase. Unfortunately, this cannot currently be done with a high degree of precision and reliability. Current automatic alignment technology is quite accurate when it comes to linking corresponding sentences in two texts that are mutual translations; see, e.g., Ve´ ronis (2000). However, performance drops substantially when automatic alignment is attempted below the sentence level; see, e.g., the results reported in (Mihalcea and Pederson, 2003). This is another reason why all commercial repetitions processors employ the full sentence as their default processing unit: by so doing, their search and retrieval can be fully automatic. (Notice, incidentally, that there is nothing to prevent subsentential segments from being manually identified and stored

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alongside their translation in the database; but then the problem is how to scale up.) Given the current state of repetitions processing technology, a reasonable question to ask, therefore, is the following: How often do complete sentences reappear verbatim in general texts? Needless to say, the question does not admit of a pat answer, in the form of a simple figure that would apply across the board to all types of texts. However, from a study that we conducted on thousands of queries submitted to an online bitextual database containing 70 million words of Canadian parliamentary debates (see Macklovitch, 2000), the answer would seem to be: not very often. More precisely, what we discovered in this study was that as the queries submitted to this database (TSrali) lengthened, the likelihood of finding an exact match decreased proportionally; when the queries reached 14 words in length, there were no longer any exact matches or repetitions. (Similar findings are reported by Langlais and Simard, 2002, who found that less than 4% of 1260 complete sentences submitted to a much larger Hansard database were repeated verbatim.) Perhaps we tend to underestimate the degree to which natural language is creative, as Noam Chomsky argued long ago in Syntactic structures. Be that as it may, what these findings suggest for repetitions processing is that any system that searches for verbatim repetitions of complete sentences will be of very limited use, except on those types of texts that happen to display a high degree of repetition, such as updates, or certain types of technical manuals in which the same commands may appear over and over again. In terms of the general worldwide demand for translation, however, these surely represent only a small proportion of the texts that need to be translated. The developers of commercial translation memory software are certainly aware of this problem and in response have attempted to extend the applicability of their systems by introducing a number of features meant to attenuate the definition of what constitutes a repetition of the same sentence. Suppose, for example, that a sentence in the text being translated matches a SL sentence in the database except for the value of a proper name or a certain numerical expression, such as a date or an amount of money. A translator would normally want to see the previous translation for this SL sentence, particularly because the named entities it contains are often left untranslated. Some translation memory systems will in fact treat the two sentences as though they were identical and may even replace the values of the named entities in the previous translation with the appropriate values from the new source sentence. Indeed, most commercial TM systems now incorporate a notion

of ‘fuzzy matching,’ which allows them to accommodate this and other sorts of nonidentical but similar SL sentences, where the degree of similarity is expressed in terms of a numerical coefficient that the user may adjust in order to constrain the search for approximate matches. How exactly do these fuzzy matching algorithms work? It is difficult to say with certainty, because TM vendors do not generally provide a formal definition of this similarity coefficient. Hence, it is not all obvious how the results of a 70% match will differ, say, from a 74% match or an 81% match. One suspects that some notion of edit distance is being employed; but when evaluating two SL strings, the matching algorithm may also take other factors into account, such as whether the translation has been produced by an MT system or by a human, or whether the database contains multiple equivalents for the same SL segment – not to mention the opaque effects of word order differences on the similarity coefficient. Combining such diverse factors into a single percentage may appear to make things simpler for the naı¨ve user; but the unfortunate result is that those same users are left with a vague and ill-defined understanding of a parameter that is central to the system. As (Hutchins, 2003a) remarked: ‘‘most TM systems have difficulty with fuzzy matching – either too many irrelevant examples are extracted, or too many potentially useful examples are missed’’ (p. 11). Another weakness in many current TM systems, attributable to the structure of the underlying database, is that in these systems, the very notion of a document is lost. When a repetitions processor submits each successive sentence of a source document to the bitextual database, it does so blindly, as it were, without any trace of the context from which it was extracted. Moreover, the contents of the database are also stored as isolated sentences, with no indication of their place in the original document. As every translator knows, however, it is not always possible to translate a sentence in isolation; the same sentence may have to be rendered differently in different documents, or even within the same document, as Be´dard (1998) convincingly argues. It is not hard to come up with examples of phenomena that are simply not amenable to translation in isolation; cross-sentence anaphora is one obvious case, but there are many others. Skeptics may argue that such problems are relatively rare, but they are missing the point. In order to evaluate a translation retrieved from memory, translators routinely need to situate that target sentence in its larger context. Current TM systems offer no straightforward way of doing this because, unlike full document archiving systems, they archive isolated sentences.

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This may seem to be excessive criticism of current translation memory systems. After all, this technology is in wide use nowadays and is generally appreciated by translators and translation service managers, unlike fully automatic machine translation systems. If we have chosen to focus on some of the shortcomings of current TM systems, rather than extolling their virtues, it is mainly to highlight the fact that their applicability remains limited to a narrow range of texts that happen to exhibit a high degree of repetition: essentially, updated documents and certain types of technical manuals. In all other document types – in the vast majority of translation situations, in other words – the repetition of complete sentences is quite rare. Hence, the usefulness of repetitions processing technology is necessarily limited, at least until such time as these systems acquire the capability of handling repetition below the level of the complete sentence. See Macklovitch and Russell (2000) for further development of this argument. Another reason for our criticism of current TM systems stems from the quasihegemony they seem to enjoy in the field of machine-aided translation. For many people, the terms translation memory and computer-assisted translation are virtually synonymous. This is regrettable, in our view, because it tends to obscure the fact that repetitions processing is just one type of translation support tool, albeit a useful one for certain kinds of repetitive documents. Nevertheless, with time, there is no doubt that we will see the emergence of many other types translation support tools, thereby relegating repetitions processors to their proper and modest place. Indeed, as we will show in the next section, there exist other ways of exploiting the very same bitextual databases on which repetitions processors are based.

Other Tools Based on Bitext Existing translations contain more solutions to more translation problems than any other existing resource (Isabelle et al., 1993).

This assertion must have appeared quite audacious when it was first published back in 1993; but if one thinks about it for a moment, recalling the hundreds of millions of words that are translated every year, it just has to be true. The real challenge, of course, is how to render all the richness lying dormant in past translations readily and easily available to human users. Repetitions processing is one way of doing this, although it is certainly not the only way. For Pierre Isabelle and his colleagues in the machineaided translation group at the CITI, the key to this challenge lay in what was then the novel concept of bitext, i.e., pairs of texts in which the translational

correspondences have been made formally explicit. In the early 1990s, progress in automatic alignment techniques had made it possible to create enormous bitextual databases that were accurately aligned at the sentence level. The question then was: How could these be exploited to help support human translators? One rather straightforward way of allowing translators and other users to benefit from databases of past translations is via an interactive bilingual concordancing tool, like the TransSearch system first developed at the CITI by Michel Simard (see Simard et al., 1993). From the user’s point of view, a concordancer is like a database querying system in which the queries correspond to various sorts of translation problems. Instead of asking colleagues if they’ve ever encountered a particular problem before and, if so, how they previously translated it, the user submits a query to the concordancer. The system responds by searching for the query in the database and displaying all the occurrences it finds, each in its full sentential context; and because this is a bitextual database, it can also display alongside each result the translation of that sentence in the target language. In that translation lies a potential solution to the original translation problem, which users may decide to recycle, as they see fit. For our present purposes, it’s interesting to note that the bitextual database that is queried via a concordancer like TransSearch has exactly the same structure as the databases that underlie commercial repetitions processing systems. (Indeed, some commercial TM packages also include an interactive concordancing facility that accesses the very same databases.) All that distinguishes the two is the manner in which they are queried. One way of expressing this difference is in terms of the trade-off that each effects between automation and flexibility. Repetitions processing provides a higher level of automation, because the system automatically submits each successive sentence in a new text to the bi-textual database, thereby ensuring that no repeated sentences are overlooked. As we have seen, however, this automation comes at the expense of a certain rigidity; because only complete sentences are submitted, repetitions of segments below the sentence level will often be ignored. An interactive concordancer, on the other hand, offers greater flexibility in the units that can be submitted – these may be any arbitrary sequence, from a single word up to a complete sentence, including ellipses – provided the user takes the initiative of manually selecting and submitting the appropriate queries. But regardless of whether the queries are submitted manually (as with a concordancer) or automatically (as with a repetitions processor), the important point to emphasize is that it is the database

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itself, that constitutes the true translation memory. For this reason, it seems legitimate to propose a more generic or neutral definition of this term, viz., a computerized archive of past translations that is structured in such a way as to promote translation reuse. Nor is it reasonable to argue that one type of translation memory is a priori superior to the other, although each may lend itself better to various configurations of repetition. For texts that exhibit a high degree of full-sentence repetition, a fully automatic repetitions processor will tend to be more suitable; otherwise, an interactive concordancer will likely prove more useful to the translator. Isabelle et al. (1993) contend that the notion of bitext provides the foundation for a whole new generation of translation support tools. One interesting and novel proposal they have made is for a translation checker, which they call TransCheck; see Macklovitch (1995) for a more detailed description of the system. As its name suggests, a translation checker is meant to be employed somewhat like a spelling or a grammar checker, to which a user will submit texts in the hope of catching typos or certain flagrant errors of grammar. The errors detected by these tools, however, are all monolingual, whereas in the case of TransCheck, the errors the system seeks to flag are bilingual, i.e., errors of correspondence between the source text and a draft translation in a target language. In order to do this, a translation checker must first transform the two texts into a bitext in which the correspondences between segments (generally sentences) are made explicit. Once this is done, the system can then verify those bitextual regions to ensure that they conform to certain wellknown properties of an acceptable translation. The properties that are currently amenable to automatic verification tend to be rather simple and straightforward; basically, they involve formal and observable features in the two texts, rather than more abstract semantic properties. So a system like TransCheck will scan the generated bitext, one aligned region at a time, and verify that each contains certain obligatory equivalences while exhibiting no prohibited equivalences. An example of the obligatory sort would be the terminological equivalences provided in a client glossary or the correct transcription of various numerical expressions; and an example of the prohibited sort would be false cognates or other types of source language interference. Needless to say, such a system will overlook many veritable translation errors, and it may flag others erroneously; but then so do monolingual grammar and spelling checkers, and this doesn’t prevent them from being helpful. In any case, the first commercial products offering rudimentary translation checking capabilities have

just begun to appear on the market; e.g., the ErrorSpy system marketed by the German firm of D.O.G.

Translator’s Workstations A translator’s workstation (TWS), conceived most generally, is a computer-based environment that integrates a number of distinct programs, all of which are intended to assist the human translator in various aspects of work. As we saw earlier, Martin Kay may have been the first to advance the idea of a TWS, which he called the translator’s amanuensis. Alan Melby was another influential advocate of this approach; see Melby (1982). A detailed chronicle of the history of the TWS is provided by Hutchins (1998), who highlights some of the lesser-known figures who first proposed various tools that have subsequently become workstation components. The point that we want to emphasize here is that the TWS is indeed an approach, much more than it is a product, and as such it admits of numerous realizations. What all such realizations have in common, of course, is the basic tenet of machine-aided human translation, namely, that it is the human who remains at the center of the translation process. The workstation provides various support tools that are designed to make the translator more productive and ideally to relieve the translator of the more fastidious tasks involved in translation. A brief word on this notion of integration, which is an essential feature of all TWS. As mentioned, a workstation may comprise disparate applications that were not necessarily intended to function together. Hence, a fundamental requirement of a welldesigned workstation is to allow the user to shift focus effortlessly from one component to another and to transfer textual data seamlessly from one application to another. To take one simple example, the user needs to constantly consult his/her terminological glossary while drafting a target text within the word processor and, if the glossary contains the desired TL equivalents, be able to directly insert those terms into the target text with a minimum of keystrokes or mouse clicks. How this is done in practice will, of course, vary from one implementation to another; but in all workstations these kinds of ergonomic considerations are of paramount importance. Today, when almost all personal computers are equipped with operating systems that feature a graphic user interface, the difficulty being referred to here may not be obvious. However, in the first workstation development projects, like the one undertaken at the CITI in the late 1980s, the available interfaces were not nearly as user friendly, and the exchange of data between applications was often a very real problem,

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due in large part to hardware memory limitations (see Macklovitch, 1989). Recall that in the incremental approach that Kay first advocated for his translator’s amanuensis, the central component was a specialized text editor; the other dedicated programs were to be gradually grafted onto this editor. Nowadays, more than two decades after Kay’s original proposal, there are an impressive number of TWSs available – although not all of them call themselves as such – that offer a variety of translation support tools. Interestingly, almost all of the commercial workstation products are being marketed by the vendors of repetitions processing technology. Hence, it is not altogether surprising that in their promotional literature it is the ‘translation memory’ that is touted as the central, productivity-enhancing component. In actual fact, all these commercial packages include other programs that render them potentially useful to translators when they are working on texts that do not exhibit a sufficient degree of full-sentence repetition; otherwise, these packages would have limited commercial appeal. Foremost among these programs is terminology management software that allows translators to create, update, and share term glossaries. The more sophisticated commercial workstations may also include automatic alignment programs, term extraction programs that operate on the bitexts that these create from legacy translations, and programs that automatically identify terms in a source text that are found within the term glossary. One significant dividing point among the leading commercial workstations is how they interact with the major word processing packages (read MS-Word). Some TWSs do this by integrating their components directly into Word, via the addition of a toolbar menu, for example. This is said to ease the learning curve for users who are already familiar with Word, while allowing them to access its many useful ancillary features, e.g., monolingual spelling and grammar checking. Trados, which is the current market leader in commercial CAT tools, has adopted this approach, as have some of its smaller competitors, such as LogiTerm. Other workstation packages furnish their own proprietary editors, presumably because this allows them greater autonomy and perhaps better component integration; but they also provide filters to facilitate the importing and exporting of text with the major word processing packages. This is the approach adopted by Atril’s De´ ja`-Vu and by Star group’s Transit product. These commercial workstation packages are now so widespread that one is tempted to say that they dominate the translation landscape. Currently, they all offer more or less the same range of functionalities, most of which have already been touched on in this

article. However, at least two other features do deserve to be mentioned, even though (strictly speaking) they may fall outside our scope. The first are sourcetext analysis programs, which provide statistics on a new text to be translated, most notably the degree of sentence repetition; these statistics are meant to allow for an informed decision on the cost-effectiveness of using repetitions processing on a particular text. The second is direct access to a fully automatic machine translation program. This figured prominently in Melby’s original proposal for a multilevel system of translation aids (see Melby, 1982), where full-scale MT was the third, or ultimate, level of automated assistance, just as it was in the historically related ALPS system described in Hutchins (1998). In the 1990s, several major vendors of translation memory technology sought to team up with the developers of commercial MT systems to provide what was vaunted as being the best of both worlds. In these integrated environments, so the argument went, a new input sentence for which no match was found in the translation memory could be sent off for automatic translation by the MT system, after which the translator need only postedit the machine output. (The sequencing of the two processes was significant, the underlying assumption being that past human translations are always preferable to those generated by machine.) There seems to be far less emphasis on such links to full MT systems today. Indeed, of the major CAT system vendors, SDL may be the only one that still offers this option, and even then quite discreetly. One may speculate on the reasons for this important change of strategy. No doubt it had something to do with the quality of the raw translations produced by the large rule-based MT systems that were dominant in the last decade, a level of quality that was probably judged to be insufficient for publication purposes and incompatible with the exacting requirements of professional translators. Indeed, several of those large commercial MT systems have since disappeared or are now targeting very different markets.

Conclusion In the last few years, there has been much encouraging progress in the general field of translation automation. In terms of core technologies, the new statistical or data-driven methods have now made it possible to develop MT systems with a fraction of the human effort that was formerly required. Moreover, the quality of the translations produced by these corpusbased systems has been steadily improving, to the point that they can now rival that produced by the best rule-based systems. In terms of attitudes as well, there has been significant progress: progress in our

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understanding of which types of technology are most suitable for different kinds of translation demands. The general distinction between translation for assimilation purposes versus translation for dissemination or publication purposes is now widely accepted and with it, the recognition that fully automatic MT is often not appropriate for the latter type of translation demand, at least not given the current state of the technology. This is a most welcome development. For too many years, the ‘‘all or nothing syndrome,’’ as Alan Melby once called it – ‘‘the attitude that the machine must translate every sentence or it is not worth using a machine at all’’ (Melby, 1982: 215) – resulted in the misdirection of energies and resources among researchers and developers, as well as producing a high level of frustration among users, who felt that automated ‘solutions’ were being imposed on them that simply did not fit their needs. In the light of this inglorious history, it is altogether refreshing to read the following from someone as informed as John Hutchins: ‘‘MT systems are not suitable for use by professional translators, who do not like to have to correct the irritatingly ‘naı¨ve’ mistakes made by computer programs. They prefer computer aids that are under their full control’’ (Hutchins, 2003b: 509). Last but not least, it is also encouraging to see that new types of translation aids are finally being added to commercial workstations, beyond the now standard repetitions processing and glossary management programs. To illustrate with one simple example, several CAT packages now include a program that seeks to detect terms that have been translated inconsistently, i.e., not as the target equivalent specified in the term glossary. Small matter that just such a program was proposed nearly 10 years ago as part of the TransCheck system. What’s important for working translators and revisers is that this kind of automated assistance, which will help relieve them of a particularly fastidious aspect of their work, is now becoming available. Of course, checking for terminological consistency is but one small element in the overall job of quality assurance in translation. Again, small matter! As Kay so aptly stated so many years ago, in machineaided translation, it’s little steps for little feet. After years of immobility, the good news is that the feet are finally moving, and moving, it would seem, in the right direction. See also: Bar-Hillel, Yehoshua (1915–1975); Kay, Martin

(b. 1935); Machine Translation: History; Machine Translation: Overview; Part-of-Speech Tagging; Terminology, Term Banks and Termbases for Translation; Translation Memories; Translation: Profession; Writers’ Aids.

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Machine Translation: Overview P Isabelle and G Foster, National Research Council of Canada, Gatineau, Quebec, Canada ß 2006 Elsevier Ltd. All rights reserved.

Introduction The term machine translation (MT) is used to refer to any process in which a machine performs a translation operation between two ordinary human languages: the source language (SL) and the target language (TL). This is the sense that appears in the following proposition: Machine translation is cheaper than human translation but less reliable.

The term can also designate the product of such a process, as in: Reading machine translations is not very pleasant but it is a convenient way to get the gist of foreign-language Web pages.

Finally, machine translation can also refer to the study of methods and techniques that render machines capable of producing (better) translations, as in the title of the present article. The term applies equally well to written or spoken language, but there is a tendency to prefer the terms speech translation or speech-to-speech translation for referring specifically to machine translation of spoken language. Translation is a very effective way of helping people communicate across the linguistic barriers. Unfortunately, human translation is expensive enough that it cannot constitute a practical solution to the everyday needs of ordinary people. As the price of human translation is unlikely to fall substantially, machine translation constitutes our best hope of making translation affordable for all.

The idea of using machines to translate is very old, but it was only around 1950, with the advent of digital computers, that serious work could really start. The initial enthusiasm led many to believe that good-quality machine translation was just around the corner, but they were wrong. After some 50 years of research, we can affirm that, barring an unexpected breakthrough, machines will not be able to compete with human translators in the foreseeable future. This prediction applies not only to difficult material such as literary works, but also to all but the very simplest and repetitive texts (e.g., weather reports). The present article explains why this is so. We start from a simplistic conception of machine translation and show where it breaks down. Then we show how computational linguists tried to fix the problems through increasingly elaborate approaches. But these more elaborate approaches turn out to raise their own additional problems.

Why Machine Translation Is a Difficult Problem Let’s assume a very naı¨ve theory: translating between human languages is just a matter of looking up the words of the source text in a bilingual dictionary. The next 12 subsections examine the many ways that simplistic theory breaks down and show why translation requires: (a) a fine-grained understanding of the source text; (b) contextual knowledge that makes it possible to fill information gaps between the SL and the TL; and (c) a detailed knowledge of the grammar of the TL. Segmenting Texts into Words

Before a dictionary search can be performed, the text needs to be segmented into a sequence of individual