A Matching Technique in Example-Based Machine Translation

A MATCIIING TECHNIQUE IN EXAMPLE,-BASED MACIIINE TRANSLATION Lambros CRANIAS, Harris PAPAGEORGIOU, Stelios PIPERIDIS Institute for Language and Speech Processing, GREECE Stelios. Piperidi~urokom. ie ABSTRACT This paper addresses an important problem in Example-Based Machine Translation (EBMT), namely how to measure similarity between a sentence fragment and a set of stored examples. A new method is proposed that measures similarity according to both surface structure and content. A second contribution is the use of clustering to make retrieval of the best matching example from the database more efficient. Results on a large number of test cases from the CELEX database are presented. 1. INTRODUCTION EBMT is based on the idea of performing translation by imitating translation examples of similar sentences [Nagao 84]. In this type of translation system, a large amount of bi/multi-lingual translation examples has been stored in a textual database and input expressions are rendered in the target language by retrieving from the database that example which is most similar to the input. There are three key issues which pertain to example-based translation : ® establishment of correspondence between units in a bi/multi-lingual text at sentence, phrase or word level • a mechanism for retrieving from the database the unit that best matches the input • exploiting the retrieved translation example to produce the actual translation of the input sentence [Brown 91] and [Gale 91] have prolx~Sed methods for establishing correspondence between sentences in bilingual corpora. [Brown 93], [Sadler 901 and [Kaji 92] have tackled the problem of establishing correspondences between words and phrases in bilingual texts. The third key issue of EBMT, that is exploiting the retrieved translation example, is usually dealt with by integrating into the system conventional MT techniques [Kaji 92], [Sumita 91]. Simple modifications of the translation proposal, such as word substitution, would also be possible, provided that alignment of the translation archive at word level was awdlable. In establishing a mechanism for the best match retrieval, which is the topic of this paper, the crucial tasks are: (i) determining whether the search is for matches at sentence or sub-sentence level, that is determining the "text unit", and (ii) the definition of the metric of similarity between two text units. As far as (i) is concerned, the olwious choice is to use as text unit the sentence. This is because, not only are sentence Ixmndaries unambiguous hut also translation propo~ls at sentence level is what a translator is usually looking for. Sentences can, however, be quite long. And the longer they are, the less possible it is that they will have an exact match in the translation archive, and the less flexible the EBMT system will be. On the other hand if the text unit is the sub-sentence we lace one major problem, that is the possibility that the resulting translation of the whole sentence will be of low quality, due to Ixmndary friction and incorrect chunking. In practice, EBMT systems that operate at sub-sentence level involve the dynamic derivation of the optimum length of segments …

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