Example-based machine translation: a review and commentary
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paper_title: Review Article: Example-based Machine Translation

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In the last ten years there has been a significant amount ofresearch in Machine Translation within a ``new'' paradigm ofempirical approaches, often labelled collectively as``Example-based'' approaches. The first manifestation of thisapproach caused some surprise and hostility among observers moreused to different ways of working, but the techniques were quicklyadopted and adapted by many researchers, often creating hybridsystems. This paper reviews the various research efforts withinthis paradigm reported to date, and attempts a categorisation ofdifferent manifestations of the general approach.
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paper_title: Shake-and-Bake Translation

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Publisher Summary ::: This chapter presents a novel approach to Machine Translation (MT), called Shake-and-Bake translation, which is based on the idea of grammars as systems of constraints. Furthermore, in this approach translation equivalence is stipulated among bags of basic expressions of the languages concerned as presented in monolingual lexicalist grammars. Equivalence is constrained by equating variables in the logical forms of equivalent signs, providing a clear semantic basis for the notion of translation equivalence, and by any other constraints the bilingual lexicographer sees fit to include. Finally, the approach allows for the target language expressions to combine freely during generation, providing trivial accounts of phenomena for which structure-based approaches postulate complex transfer, noncompositional grammars, or undecidable logical inferences.
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paper_title: Translating with Examples: A New Approach to Machine Translation

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This paper proposes Example-Based Machine Translation (EBMT). EBMT retrieves similar examples (pairs of source texts and their translations) from the database, adapting the examples to translate a new source text. This paper compares the various costs of EBMT and conventional Rule-Based Machine Translation (RBMT). It explains EBMT's new features which RBMT lacks, and describes its configuration and the basic computing mechanism. In order to demonstrate EBMT's feasibility, the translation of Japanese noun phrases of the form "N1 no N2" to English noun phrases is explained in detail. Translation of other parts of Japanese sentences, including "da" sentences , aspect, and idiomatic expressions, as well as the integration of EBMT with RBMT are discussed.
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paper_title: Non-Hybrid Example-Based Machine Translation Architectures

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A general definition of rationalist and empiricist natural language processing is attempted. A classification of empiricist machine translation systems is given based on the rationalist/empiricist distinction. Examples of approaches falling into the two different strategies are discussed. Research results are reported from attempts to break new ground in what is referred to as "pure" or non-hybrid example-based machine translation.
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paper_title: Learning Translation Templates From Bilingual Text

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This paper proposes a two-phase example-based machine translation methodology which develops translation templates from examples and then translates using template matching. This method improves translation quality and facilitates customization of machine translation systems. This paper focuses on the automatic learning of translation templates. A translation template is a bilingual pair of sentences in which corresponding units (words and pharases) are coupled and replaced with variables. Correspondence between units is determined by suing a bilingual dictionary and by analyzing the syntactic structure of the sentences. Syntactic ambiguity and ambiguity in correspondence between units are simultaneously resolved. All of the translation templates generated from a bilingual corpus are grouped by their source language part, and then further refined to resolved conflicts among templates whose source language parts are the same but whose target language parts are different. By using the proposed method, not only transfer rules but also knowledge for lexical selection is effectively extracted from a bilingual corpus.
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paper_title: Robust Large-Scale EBMT with Marker-Based Segmentation

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Previous work on marker-based EBMT [Gough & Way, 2003, Way & Gough, 2004] suffered from problems such as data-sparseness and disparity between the training and test data. We have developed a large-scale robust EBMT system. In a comparison with the systems listed in [Somers, 2003], ours is the third largest EBMT system and certainly the largest English-French EBMT system. Previous work used the on-line MT system Logomedia to translate source language material as a means of populating the system’s database where bitexts were unavailable. We derive our sententially aligned strings from a Sun Translation Memory (TM) and limit the integration of Logomedia to the derivation of our word-level lexicon. We also use Logomedia to provide a baseline comparison for our system and observe that we ::: outperform Logomedia and previous marker-based EBMT systems in a number of tests.
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paper_title: Combining Dictionary-Based and Example-Based Methods for Natural Language Analysis

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We propose combining dictionary-based and example-based natural language (NL) processing techniques in a framework that we believe will provide substantive enhancements to NL analysis systems. The centerpiece of this framework is a relatively large-scale lexical knowledge base that we have constructed automatically from an online version of Longman's Dictionary of Contemporary English (LDOCE), and that is currently used in our NL analysis system to direct phrasal attachments. After discussing the effective use of example-based processing in hybrid NL systems, we compare recent dictionarybased and example-based work, and identify the aspects of this work that are included in the proposed framework. We then describe the methods employed in automatically creating our lexical knowledge base from LDOCE, and its current and planned use as a large-scale example base in our NL analysis system. This knowledge base is structured as a highly interconnected network of words linked by semantic relations such as is_a, has_part, location_of, typical_object, and is_for. We claim that within the proposed hybrid framework, it provides a uniquely rich source of information for use during NL analysis.
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paper_title: Example-Based Controlled Translation

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The first research on integrating controlled language data in an Example-Based Machine Translation (EBMT) system was published in [Gough & Way, 2003]. We improve on their sub-sentential alignment algorithm to populate the system’s databases with more than six times as many potentially useful fragments. Together with two simple novel improvements—correcting mistranslations in the lexicon, and allowing multiple translations in the lexicon—translation quality improves considerably when target language ::: translations are constrained. We also develop the first EBMT system which attempts to filter the source language data using controlled language specifications. We provide ::: detailed automatic and human evaluations of a number of experiments carried out to test the quality of the system. We observe that our system outperforms Logomedia in a number of tests. Finally, despite conflicting results from different automatic evaluation metrics, we observe a preference for controlling the source data rather than the target translations.
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paper_title: Controlled Translation in an Example-based Environment: What do Automatic Evaluation Metrics Tell Us?

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This paper presents an extended, harmonised account of our previous work on integrating controlled language data in an Example-based Machine Translation system. Gough and Way in MT Summit pp. 133---140 (2003) focused on controlling the output text in a novel manner, while Gough and Way (9th Workshop of the EAMT, (2004a), pp. 73---81) sought to constrain the input strings according to controlled language specifications. Our original sub-sentential alignment algorithm could deal only with 1:1 matches, but subsequent refinements enabled n:m alignments to be captured. A direct consequence was that we were able to populate the system's databases with more than six times as many potentially useful fragments. Together with two simple novel improvements --- correcting a small number of mistranslations in the lexicon, and allowing multiple translations in the lexicon --- translation quality improves considerably. We provide detailed automatic and human evaluations of a number of experiments carried out to test the quality of the system. We observe that our system outperforms the rule-based on-line system Logomedia on a range of automatic evaluation metrics, and that the `best' translation candidate is consistently highly ranked by our system. Finally, we note in a number of tests that the BLEU metric gives objectively different results than other automatic evaluation metrics and a manual evaluation. Despite these conflicting results, we observe a preference for controlling the source data rather than the target translations.
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paper_title: Comparing example-based and statistical machine translation

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In previous work (Gough and Way 2004), we showed that our Example-Based Machine Translation (EBMT) system improved with respect to both coverage and quality when seeded with increasing amounts of training data, so that it significantly outperformed the on-line MT system Logomedia according to a wide variety of automatic evaluation metrics. While it is perhaps unsurprising that system performance is correlated with the amount of training data, we address in this paper the question of whether a large-scale, robust EBMT system such as ours can outperform a Statistical Machine Translation (SMT) system. We obtained a large English-French translation memory from Sun Microsystems from which we randomly extracted a near 4K test set. The remaining data was split into three training sets, of roughly 50K, 100K and 200K sentence-pairs in order to measure the effect of increasing the size of the training data on the performance of the two systems. Our main observation is that contrary to perceived wisdom in the field, there appears to be little substance to the claim that SMT systems are guaranteed to outperform EBMT systems when confronted with ‘enough’ training data. Our tests on a 4.8 million word bitext indicate that while SMT appears to outperform our system for French-English on a number of metrics, for English-French, on all but one automatic evaluation metric, the performance of our EBMT system is superior to the baseline SMT model.
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Title: Example-based machine translation: a review and commentary

Section 1: General introduction
Description 1: Provide an overview of the evolution and contrast among rule-based, statistical, and example-based machine translation models.

Section 2: Contents of the collection
Description 2: Discuss the aims of the collection, the editors' perspective on defining EBMT, and the structure of the compiled articles.

Section 3: Foundations of EBMT
Description 3: Summarize foundational articles discussing the distinctive features of EBMT and comparisons with RBMT and SMT.

Section 4: Run-time approaches to EBMT
Description 4: Describe various EBMT systems that make direct use of bilingual data during the translation process.

Section 5: Template-driven EBMT
Description 5: Outline methods of building and using templates from bilingual corpora in EBMT systems.

Section 6: EBMT and derivation trees
Description 6: Detail approaches that use more structured templates derived from bilingual corpora.

Section 7: General comments and observations
Description 7: Address the overall coherence of EBMT systems, defining EBMT, and its relation to RBMT and SMT.

Section 8: Defining EBMT
Description 8: Explore attempts to define EBMT, its core processes, and how it differs from SMT and RBMT.

Section 9: Methods and problems in EBMT
Description 9: Discuss specific example-based methods, handling of templates, semantic disambiguation, and issues encountered.

Section 10: Complexity and expandability
Description 10: Evaluate the complexity of EBMT systems, scalability issues, and the potential for expansion.

Section 11: Hospitality and hybridization
Description 11: Examine the integration of various techniques within EBMT and its status as a hybrid MT approach.

Section 12: Evaluation and commercialization
Description 12: Assess the performance of EBMT systems, current evaluation methods, and prospects for commercialization.