[Please send any comments to Robert Frederking (ref+@cs.cmu.edu, Web document maintainer) or Ed Hovy or Nancy Ide.]

Nuria Bel

Bonnie Dorr

Eduard Hovy

Kevin Knight

Hitoshi Iida

Christian Boitet

Bente Maegaard

Yorick Wilks

The term machine translation (MT) is normally taken in its restricted and precise meaning of fully automatic translation. However, in this chapter we consider the whole range of tools that may support translation and document production in general, which is especially important when considering the integration of other language processing techniques and resources with MT. We therefore define Machine Translation to include any computer-based process that transforms (or helps a user to transform) written text from one human language into another. We define Fully Automated Machine Translation (FAMT) to be MT performed without the intervention of a human being during the process. Human-Assisted Machine Translation (HAMT) is the style of translation in which a computer system does most of the translation, appealing in case of difficulty to a (mono- or bilingual) human for help. Machine-Aided Translation (MAT) is the style of translation in which a human does most of the work but uses one of more computer systems, mainly as resources such as dictionaries and spelling checkers, as assistants.

Traditionally, two very different classes of MT have been identified. Assimilation refers to the class of translation in which an individual or organization wants to gather material written by others in a variety of languages and convert them all into his or her own language. Dissemination refers to the class in which an individual or organization wants to broadcast his or her own material, written in one language, in a variety of language to the world. A third class of translation has also recently become evident. Communication refers to the class in which two or more individuals are in more or less immediate interaction, typically via email or otherwise online, with an MT system mediating between them. Each class of translation has very different features, is best supported by different underlying technology, and is to be evaluated according to somewhat different criteria.

Machine Translation was the first computer-based application related to natural language, starting after World War II, when Warren Weaver suggested using ideas from cryptography and information theory. The first large-scale project was funded by the US Government to translate Russian Air Force manuals into English. After a decade of initial optimism, funding for MT research became harder to obtain in the US. However, MT research continued to flourish in Europe and then, during the 1970s, in Japan. Today, over 50 companies worldwide produce and sell translations by computer, whether as translation services to outsiders, as in-house translation bureaux, or as providers of online multilingual chat rooms. By some estimates, MT expenditure in 1989 was over $20 million worldwide, involving 200—300 million pages per year (Wilks 92).

Ten years ago, the typical users of machine translation were large organizations such as the European Commission, the US Government, the Pan American Health Organization, Xerox, Fujitsu, etc. Fewer small companies or freelance translators used MT, although translation tools such as online dictionaries were becoming more popular. However, ongoing commercial successes in Europe, Asia, and North America continued to illustrate that, despite imperfect levels of achievement, the levels of quality being produced by FAMT and HAMT systems did address some users’ real needs. Systems were being produced and sold by companies such as Fujitsu, NEC, Hitachi, and others in Japan, Siemens and others in Europe, and Systran, Globalink, and Logos in North America (not to mentioned the unprecedented growth of cheap, rather simple MT assistant tools such as PowerTranslator).

In response, the European Commission funded the Europe-wide MT research project Eurotra, which involved representatives from most of the European languages, to develop a large multilingual MT system (Johnson, et al., 1985). Eurotra, which ended in the early 1990s, had the important effect of establishing Computational Linguistics groups in a several countries where none had existed before. Following this effort, and responding to the promise of statistics-based techniques (as introduced into Computational Linguistics by the IBM group with their MT system CANDIDE), the US Government funded a four-year effort, pitting three theoretical approaches against each other in a frequently evaluated research program. The CANDIDE system (Brown et al., 1990), taking a purely-statistical approach, stood in contrast to the Pangloss system (Frederking et al., 1994), which initially was formulated as a HAMT system using a symbolic-linguistic approach involving an interlingua; complementing these two was the LingStat system (Yamron et al., 1994), which sought to combine statistical and symbolic/linguistic approaches. As we reach the end of the decade, the only large-scale multi-year research project on MT worldwide is Verbmobil in Germany (Niemann et al., 1997), which focuses on speech-to-speech translation of dialogues in the rather narrow domain of scheduling meetings.

In organizations such as European Commission, large integrated environments have been built around MT systems; cf. the European Commission Translation Service’s Euramis (Theologitis, 1997).

The use of tools for translation by freelancers and smaller organizations is developing quickly. Cheap translation assistants, often little more than bilingual lexicons with rudimentary morphological analysis and some text processing capability, are making their way to market to help small companies and individuals write foreign letters, email, and business reports. Even the older, more established systems such as Globalink, Logos, and Systran, offer pared-down PC-based systems for under $500 per language pair. The Machine Translation Compendium available from the International Association of MT (Hutchins, 1999) lists over 77 pages of commercial MT systems for over 30 languages, including Zulu, Ukrainian, Dutch, Swahili, and Norwegian.

General purpose vs. Domain-specific: Most (commercial) systems are meant to be general purpose. Although the performance is actually not always very good, the systems are used anyway. However, if the systems were better, MT would be used a whole lot more--given the explosion of information in the world, the demand for translation is booming, and the only possible answer to this demand is MT (in all its forms).

Domain-specific systems deliver better performance, as they can be tailor-made to specific text types. TAUM-METEO, for example, contains a lexicon of only 220 words, and produces translations of weather reports at 98% accuracy; PaTrans (Maegaard and Hansen, 1995) translates abstracts of chemical reports at high quality. However, domain specific systems exhibit two drawbacks: they are only cost-effective in large-volume domains, and maintaining many domain-specific systems may not be manageable; cf. Section 4.3.3 below.

One of the most pressing questions of MT results from the recent introduction of a new paradigm into Computational Linguistics. It had always been thought that MT, which combines the complexities of two languages (at least), requires highly sophisticated theories of linguistics in order to produce reasonable quality output.

As described above, the CANDIDE system (Brown et al., 1990) challenged that view. The DARPA MT Evaluation series of four MT evaluations, the last of which was held in 1994, compared the performance of three research systems, more than 5 commercial systems, and two human translators (White et al., 1992—94). It forever changed the face of MT, showing that MT systems using statistical techniques to gather their rules of cross-language correspondence were feasible competitors to traditional, purely hand-built ones. However, CANDIDE did not convince the community that the statistics-only approach was the optimal path; in developments since 1994, it has included steadily more knowledge derived from linguistics. This left the burning question: which aspects of MT systems are best approached by statistical methods, and which by traditional, linguistic ones?

Since 1994, a new generation of research MT systems is investigating various hybridizations of statistical and symbolic techniques (Knight et al., 1995; Brown and Frederking, 1995; Dorr , 1997; Nirenburg et al., 1992; Wahlster, 1993; Kay et al., 1994). While it is clear by now that some modules are best approached under one paradigm or the other, it is a relatively safe bet that others are genuinely hermaphroditic, and that their best design and deployment will be determined by the eventual use of the system in the world. Given the large variety of phenomena inherent in language, it is highly unlikely that there exists a single method to handle all the phenomena--both in the data/rule collection stage and in the data/rule application (translation) stage--optimally. Thus one can expect all future non-toy MT systems to be hybrids. Methods of statistics and probability combination will predominate where robustness and wide coverage are at issue, while generalizations of linguistic phenomena, symbol manipulation, and structure creation and transformation will predominate where fine nuances (i.e., translation quality) are important. Just as we today have limousines, trucks, passenger cars, trolley buses, and bulldozers, just so we will have different kind of MT systems that use different translation engines and concentrate on different functions.

One way to summarize the essential variations is as follows:

Feature Symbolic Statistical

robustness/coverage: lower higher

quality/fluency: higher lower

representation: deeper shallower

How exactly to combine modules into systems, however, remains a challenging puzzle. As argued in (Church and Hovy, 1993), one can use MT function to identify productive areas for guiding research. The `niches of functionality’ provide clearly identifiable MT goals. Major applications include:

assimilation tasks: lower quality, broad domains – statistical techniques predominate

dissemination tasks: higher quality, limited domains – symbolic techniques predominate

communication tasks: medium quality, medium domain – mixed techniques predominate

Most production systems are rule-based. That is, they consist of grammar rules, lexical rules, etc. More rules lead to more sophistication and more complexity, and may in the end develop into systems that are quite difficult to maintain. (Typical commercial MT systems contain between a quarter and a half million words and 500—1000 grammar rules for each of the more complex languages.) Consequently, alternative methods have been sought.

Translation by analogy, usually called memory-based or example-based translation (EBMT), see (Nagao, 1984), is one answer to this problem. An analogy-based translation system has pairs of bilingual expressions stored in an example database. The source language input expression is matched against the source language examples in the database, and the best match is chosen. The system then returns the target language equivalent of this example as output, i.e., the best match is based only on the source database, different translations of the source are not taken into account. Just as for translation memories, the analogy-based translation builds on approved translations, consequently the quality of the output is expected to be high.

Unfortunately, however, purely analogy-based systems have problems with scalability: the database becomes too large and unmanageable for systems with a realistic coverage. Consequently, a combination of the rule-based approach and the analogy-based approach is the solution. We are seeing many proposals for such hybrid solutions and this is certainly one of the areas that will bring practical MT further.

Current rule-based MT uses either the Transfer architecture or the Interlingua architecture. These approaches can be diagrammed as:

Interlingua approach:

Transfer approach:

The IntermediateStructure is a (usually grammatical) analysis of the text, one sentence at a time. The Interlingua is a (putatively) language-neutral analysis of the text. The theoretical advantage of the Interlingua approach is that one can add new languages at relatively low cost, by creating only rules mapping from the new language into the Interlingua and back again. In contrast, the Transfer approach requires one to build mapping rules from the new language to and from each other language in the system.

The Transfer approach involves a comparison between just the two languages involved. The transfer phase exactly compares lexical units and syntactic structures across the language gap and uses mapping rules to convert the source IntermediateStructure into the target IntermediateStructure representation (Tsujii, 1990). These rules, plus any additional semantic or other information, are stored in dictionaries or knowledge bases. In the transfer approach, nothing is decided a priori about the depth of analysis, i.e., the depth of analysis can depend on the closeness of the languages involved--the closer the languages, the shallower the analysis.

However, for high quality translations, syntactic analysis or shallow semantic analysis is often not enough. Effective translation may require the system to ‘understand’ the actual meaning of the sentence. For example, "I am small" is expressed in many languages using the verb "to be", but "I am hungry" is often expressed using the verb "to have", as in "I have hunger". For a translation system to handle such cases (and their more complex variants), it needs to have information about hunger and so on. Often, this kind of information is represented in so-called case frames, small collections of attributes and their values. The translation system then requires an additional analysis module, usually called the semantic analyzer, additional (semantic) transfer rules, and additional rules for the realizer. The semantic analyzer produces a case frame from the syntax tree, and the transfer module converts the case frame derived from the source language sentence into the case frame format of the target language.

As mentioned above, the Interlingua approach requires less work to add a new language than the Transfer approach. However, to date no convincing large-scale Interlingua notation has yet been built. All interlingual MT systems to date have operated at the scale of demonstration (a few hundred lexical items) or prototype (a few thousand). Though a great deal has been written about interlinguas, but no clear methodology exists for determining exactly how one should build a true language-neutral meaning representation, if such a thing is possible at all (Whorf, 1956; Nirenburg et al., 1992; Hovy and Nirenburg, 1992; Dorr, 1994).

In practical systems, the transfer approach is often chosen simply because it is the simplest and scales up the best. This is an important virtue in the development of production systems. However, researchers will continue to pursue the Interlingual approach for a variety of reasons. Not only does it hold the promise of decreasing the cost of adding a new language, but it also encourages the inclusion of deeper, more abstract levels of representation, including discourse structure and interpersonal pragmatics, than are included in transfer structures.

In recent years, several different methods of performing MT–transfer, example-based, simple dictionary lookup, etc.–have all shown their worth in the appropriate circumstances. A promising recent development has been the attempt to integrate various approaches into a single multi-engine MT system. The idea is very simple: pass the sentence(s) to be translated through several MT engines in parallel, and at the end combine their output, selecting the best fragment(s) and recomposing them into the target sentence(s).

This approach makes admirable use of the strengths of each type of MT. For example, since Example-Based Translation is very effective in handling a wide variety of natural speech expressions and incomplete sentences, it is best employed when phrases or fixed subphrases are translated. However, for fully formed, complex grammatical sentences, the analysis stages typically included in the Transfer and Interlingual approaches is still required. The ATR Cooperative Integrated Translation project has constructed a multi-engine mechanism by an analytical method via a bottom-up chart parser mechanism (Maegaard and Hansen, 1995). Using this mechanism the project has realized a prototype system for multilingual translation by preparing language patterns of source language expression examples and translation examples for each language pair. The system characterized as ‘chat translation’ performs two kind of two-way translation, namely Japanese-English and Japanese-Korean, and moreover, one-way Japanese to German. It outputs the synthesized speech in these four languages. It has been designed for translating travel arrangement dialogues between an information service and tourists.

Another example of multi-engine MT is Pangloss (Frederking et al., 1994). This MT system combined a multilingual dictionary, an Example-Based engine, and a full-fledged KBMT-style Interlingua system into one framework. During translation, each engine assigned a score to each fragment of its output. After normalizing these scores, the post-translation integrator module placed all output fragments in a chart, in parallel. In early versions, the system then employed a dynamic programming algorithm to traverse the chart, select the best-scoring set of fragments (of whatever size), and combine the fragments into the resulting output sentences. Later versions employed statistical language modeling, as used in speech recognition, to combine the scores with the a priori likelihood of the resulting sequence of words (Brown and Frederking, 1995).

Some bottlenecks have already been mentioned above, especially in Section 4.3.2.

A rather depressing (for researchers) fact that we do know today can be stated as follows: generally, the older a system, the better its performance, regardless of the modernity of its internal operations. Why is this?

MT, as all NLP applications, deals with language. Language requires a lot of information: lexical, grammatical, translation equivalences, etc. Whatever way this knowledge is used (and the differences constitute the basis for the different approaches to MT), this knowledge must be instantiated (in dictionaries, in rules, in repository of examples, in collections of parallel texts) and processed. These two factors--knowledge collection and effective knowledge use--form the major bottlenecks faced nowadays not only for MT but for all NLP systems.

One approach is to talk of performant MT and NLP, rather than of MT (and NLP) in the abstract. Although systems are sometimes designed as to cope with general, unrestricted language, in the end it usually turns out that in order to make them performant some customization is required. This gives rise to problems of coverage, because it seems unlikely that either linguistic or statistical approaches alone can actually cope with all the possibilities of a given language. Where statistical systems can collect, sort, and classify large volumes of data, and can perhaps filter out uncommon or strange usage, linguistic insights are required to guide the statistical processing in order to operate at effective levels of abstraction. One does not, for example, build a statistical NLP system to consider all the words of four letters, or all the words beginning with the letter t. The ways one limits the statistics, and the linguistic levels at which one chooses to operate, both circumscribe the coverage of the system, and ultimately determine where (and if) it will be performant in practice.

Much more experience is needed in the question of statistics-based MT before it will be clear where the performance limits lie. It is clear that statistical techniques can be used effectively to overcome some of the knowledge acquisition bottlenecks–to collect words and phrases, for example. But can it be used to find concepts, those Interlingual units of meaning that are essential for high-quality translation? It is also clear that statistical methods help with some of the basic processes of MT–word segmentation, part of speech tagging, etc. But can they help with analysis, that process of sentence decomposition without which non-trivial MT is impossible?

A second bottleneck is partially addressed by the multi-engine approach. One can quite confidently assume that no single MT technique is going to provide the best answer in all cases, for all language styles. Furthermore, for applications such as text scanning, a rough translation is quite sufficient, and so a more detailed, but slower and more expensive, translation is not required. How can one best combine various MT engines, weaving together their outputs into the highest quality sentences? How can one combine experimental research systems (that may produce output only in some cases, but then do very well) with tried and true commercial systems (that always produce something, though it might be of low quality)? These questions are not necessarily deep, but they are pressing, and they should be investigated if we are to achieve true general-purpose FAMT.

For speech-to-speech translation, evaluation (Carter et al., 1997) shows that fundamental research is still badly needed to improve overall quality and increase usability, in particular on:

1. Context processing: how to transmit and use possible Centers:

• in analysis, for anaphora or elision,
• in generation, for controlling lexical selection and producing ellipses and elisions to improve naturalness and coherence.

1. Prosody processing: how to generate prosodic marks (to be used by the text to speech components) from pragmatic, semantic and syntactic features.
2. Integration between heterogeneous components (speech recognition and MT):

• richer interface data structures (such as tree lattices),
• use of common primary linguistic resources (lexical and grammatical data bases),
• system architecture (pipeline, agents, blackboard, whiteboard).

Current research focuses on almost fully automatic systems, leading to extremely specific, task-dependent systems. While they can be useful, we should not repeat the errors of the 1970s. We should focus on computerized assistance for interpreters (to help several conversations partially conducted directly in some common language or indirectly through some imperfect spoken translation system) for active listeners wanting to better understand speech in a foreign language (conversation, radio, TV).

Several applications have proven to be able to work effectively using only subsets of the knowledge required for MT. It is possible now to evaluate different tasks, to measure the information involved in solving them, and to identify the most efficient techniques for a given task. Thus, we must face the decomposition of monolithic systems, and to start talking about hybridization, engineering, architectural changes, shared modules, etc. It is important when identifying tasks to evaluate linguistic information in terms of what is generalizable, and thus a good candidate for traditional parsing techniques (argument structure of a transitive verb in active voice?), and what is idiosyncratic (what about collocations?). Besides, one cannot discard the power of efficient techniques that yield better results than older approaches, as illustrated clearly by part of speech disambiguation, which has proved to be better solved using Hidden Markov Models than traditional parsers. On the other hand, it has been proven that good theoretically motivated and linguistically driven tagging label sets improve the accuracy of statistical systems. Hence we must be ready to separate the knowledge we want to represent from the techniques/formalisms that have to process it.

Two other ongoing developments do not draw much on empirical linguistics. The first is the continuing integration of low-level MT techniques with conventional word processing to provide a range of aids, tools, lexicons, etc., for both professional and occasional translators. This is now a real market, assisting translators to perform, and confirms Martin Kay’s predictions (Kay,1997; reprint) about the role of machine-aided human translation some twenty years ago. Kay’s remarks predated the more recent empirical upsurge and seemed to reflect a deep pessimism about the ability of any form of theoretical linguistics, or theoretically motivated computational linguistics, to deliver high-quality MT. The same attitudes underlie (Arnold et al., 1994), which was produced by a group long committed to a highly abstract approach to MT that failed in the Eurotra project; the book itself is effectively an introduction to MT as an advanced form of document processing.

Second, the problem of semantics is perennial. Without some level of semantic representation, MT systems will never be able to achieve high quality, because they will never be able to differentiate between cases that are lexically and syntactically ambiguous. The abovementioned work on semantics in lexicons and ontologies will benefit MT (as it will other applications such as Summarization and Information Extraction).

Third, an increasingly pressing bottleneck is the fact that essentially all MT systems operate at the single-sentence level. Except for a few experimental attempts, no systems have explored translating beyond the sentence boundary, using discourse representations. Typically, their only cross-sentence record is a list of referents for pronominalization. Yet many phenomena in language span sentence boundaries:

• Erroneous quotation scoping: In a direct quote in Japanese, the reporting verb of the sentence (the main clause) follows the quote itself (the dependent clause), while in English it normally precedes the quote. Inverting the main and dependent clauses is manageable when the quote is a single sentence, but when it spans multiple sentences, the system currently has no way to determine at which sentence the quote began, and is hence incapable of placing the main clause correctly. As a result, quoted multi-sentence text is translated very oddly by J-E systems.
• Inadequate pronominalization: The system cannot know what personal pronoun ("he", "she", or "it") to use when its referent lies in an earlier sentence. This problem occurs especially often in J-E translation since Japanese frequently omits sentence subjects; when the system attempts to create and insert a pronoun in the English it has no knowledge of previously introduced referents and hence has no alternative but to guess a pronoun.
• Inappropriate comma insertion: Most synthesis modules contain a set of rules that govern the insertion of commas into the final English text. These rules seldom operate adequately. One reason is that comma placement in English is partially prosodic, based on the rhythm and balance of clauses in the text; without knowing the length and internal structure of the paragraph, comma insertion rules have no way of determining appropriate placement points.
• Incorrect relative pronoun selection: The choice of relative pronoun ("that", "in which", "which", "to whom", etc.) is not always trivial, and the behavior of the current synthesis rules in the system reflect that fact. Since relative pronouns refer to entities outside of the relative clause, rules for proper pronoun usage must be able to locate and inspect the appropriate referent.

Fortunately, recent developments in Text Linguistics, Discourse Study, and computational text planning have led to theories and techniques that are potentially of great importance for MT. Using a paragraph structure, one can represent and manipulate cross-sentence groupings of clauses and sentences. Marcu (1997) describes a method of automatically producing trees representing paragraph structure. Two studies report the effects of output quality using a very simple paragraph structure tree to treat multi-sentence quotes (Hovy and Gerber, 1997) and to break up overlong sentences (Gerber and Hovy, 1998).

Fourth, the treatment of so-called low-diffusion languages requires additional attention. Not all languages are equally well covered by MT; even some of the most populous nations in the world are not yet represented in the commercial (or even research) MT sphere: Indonesia, various languages of India, and others. The so-called major languages are reasonably well covered at present, and will certainly be well covered in the future, but users of less spoken languages need MT and other tools just as much or even more than users of English, Spanish, French and Japanese. For some languages the market is not sufficiently large, which means that users of those language will lack the tools which are otherwise available. This lack of tools will have an obvious economic effect, but also a cultural effect by excluding some languages from participating in an otherwise flourishing multilinguality.

The future of MT is rosy. Thanks largely to the Internet and the growth of international commerce, casual (one-off) and repeated MT is growing at a very fast pace. Correspondingly, MT products are coming to market as well. The Machine Translation Compendium (Hutchins, 1999) lists commercial products in over 30 languages (including Zulu, Ukrainian, Dutch, Swahili, and Norwegian) in 83 language pairs. Comparative studies of MT systems, including the OVUM Report (OVUM, 1995) and the ABS Study (ABS, 1999), continue to become available, although they tend to cost upward of US$1,000.

In tandem with this growth, it is imperative to ensure that research in MT begins again. At this time, neither the EU nor the North American funding agencies support coordinated, or even large separate, research projects in MT. Without research, however, the difficult problems endemic to MT will not be solved; MT companies do not have enough financial leeway or in many cases the technical expertise required to make theoretical breakthroughs. Since market forces alone cannot solve the problem, governments and funding agencies have to take an active role in the protection and reinforcement of MT.