Translating with Non-contiguous Phrases

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 755–762, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Translating with non-contiguous phrases Michel Simard, Nicola Cancedda, Bruno Cavestro, Marc Dymetman, Eric Gaussier, Cyril Goutte, Kenji Yamada Xerox Research Centre Europe FirstName.FamilyName@xrce.xerox.com Philippe Langlais RALI/DIRO Universit´e de Montr´eal felipe@iro.umontreal.ca Arne Mauser RWTH Aachen University arne.mauser@rwth-aachen.de Abstract This paper presents a phrase-based statis- tical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. A method for producing such phrases from a word-aligned corpora is proposed. A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation met- ric. Translations are produced by means of a beam-search decoder. Experimental re- sults are presented, that demonstrate how the proposed method allows to better gen- eralize from the training data. 1 Introduction Possibly the most remarkable evolution of recent years in statistical machine translation is the step from word-based models to phrase-based models (Och et al., 1999; Marcu and Wong, 2002; Yamada and Knight, 2002; Tillmann and Xia, 2003). While in traditional word-based statistical models (Brown et al., 1993) the atomic unit that translation operates on is the word, phrase-based methods acknowledge the significant role played in language by multi- word expressions, thus incorporating in a statistical framework the insight behind Example-Based Ma- chine Translation (Somers, 1999). However, Phrase-based models proposed so far only deal with multi-word units that are sequences of contiguous words on both the source and the tar- get side. We propose here a model designed to deal with multi-word expressions that need not be con- tiguous in either or both the source and the target side. The rest of this paper is organised as follows. Sec- tion 2 provides motivations, definition and extrac- tion procedure for non-contiguous phrases. The log- linear conditional translation model we adopted is the object of Section 3; the method used to train its parameters is described in Section 4. Section 5 briefly describes the decoder. The experiments we conducted to asses the effectiveness of using non- contiguous phrases are presented in Section 6. 2 Non-contiguous phrases Why should it be a good thing to use phrases composed of possibly non-contiguous sequences of words? In doing so we expect to improve trans- lation quality by better accounting for additional linguistic phenomena as well as by extending the effect of contextual semantic disambiguation and example-based translation inherent in phrase-based MT. An example of a phenomenon best described using non-contiguous units is provided by English phrasal verbs. Consider the sentence “Mary switches her table lamp off”. Word-based statistical mod- els would be at odds when selecting the appropri- ate translation of the verb. If French were the target language, for instance, corpus evidence would come from both examples in which “switch” is translated as “allumer” (to switch on) and as “´eteindre” (to switch off). If many-to-one word alignments are not …

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