Consistent Translation using Discriminative Learning - A Translation Memory-inspired Approach

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1239–1248, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Consistent Translation using Discriminative Learning: A Translation Memory-inspired Approach∗ Yanjun Ma† Yifan He‡ Andy Way‡ Josef van Genabith‡ † Baidu Inc., Beijing, China yma@baidu.com ‡Centre for Next Generation Localisation School of Computing, Dublin City University {yhe,away,josef}@computing.dcu.ie Abstract We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are widely used by human translators in industrial settings. We constrain the translation of an in- put sentence using the most similar ‘transla- tion example’ retrieved from the TM. Differ- ently from previous research which used sim- ple fuzzy match thresholds, these constraints are imposed using discriminative learning to optimise the translation performance. We ob- serve that using this method can benefit the SMT system by not only producing consis- tent translations, but also improved translation outputs. We report a 0.9 point improvement in terms of BLEU score on English–Chinese technical documents. 1 Introduction Translation consistency is an important factor for large-scale translation, especially for domain- specific translations in an industrial environment. For example, in the translation of technical docu- ments, lexical as well as structural consistency is es- sential to produce a fluent target-language sentence. Moreover, even in the case of translation errors, con- sistency in the errors (e.g. repetitive error patterns) are easier to diagnose and subsequently correct by translators. ∗This work was done while the first author was in the Cen- tre for Next Generation Localisation at Dublin City University. In phrase-based SMT, translation models and lan- guage models are automatically learned and/or gen- eralised from the training data, and a translation is produced by maximising a weighted combination of these models. Given that global contextual informa- tion is not normally incorporated, and that training data is usually noisy in nature, there is no guaran- tee that an SMT system can produce translations in a consistent manner. On the other hand, TM systems – widely used by translators in industrial environments for enterprise localisation by translators – can shed some light on mitigating this limitation. TM systems can assist translators by retrieving and displaying previously translated similar ‘example’ sentences (displayed as source-target pairs, widely called ‘fuzzy matches’ in the localisation industry (Sikes, 2007)). In TM sys- tems, fuzzy matches are retrieved by calculating the similarity or the so-called ‘fuzzy match score’ (rang- ing from 0 to 1 with 0 indicating no matches and 1 indicating a full match) between the input sentence and sentences in the source side of the translation memory. When presented with fuzzy matches, translators can then avail of useful chunks in previous transla- tions while composing the translation of a new sen- tence. Most translators only consider a few sen- tences that are most similar to the current input sen- tence; this process can inherently improve the con- sistency of translation, given that …

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