Refined Lexicon Models for Statistical Machine Translation using a Maximum Entropy Approach

Refined Lexicon Models for Statistical Machine Translation using a Maximum Entropy Approach Ismael Garc´ıa Varea Dpto. de Inform´atica Univ. de Castilla-La Mancha Campus Universitario s/n 02071 Albacete, Spain ivarea@info-ab.uclm.es Franz J. Och and Hermann Ney Lehrstuhl f¨ur Inf. VI RWTH Aachen Ahornstr., 55 D-52056 Aachen, Germany � och|ney � @cs.rwth-aachen.de Francisco Casacuberta Dpto. de Sist. Inf. y Comp. Inst. Tecn. de Inf. (UPV) Avda. de Los Naranjos, s/n 46071 Valencia, Spain fcn@iti.upv.es Abstract Typically, the lexicon models used in statistical machine translation systems do not include any kind of linguistic or contextual information, which often leads to problems in performing a cor- rect word sense disambiguation. One way to deal with this problem within the statistical framework is to use max- imum entropy methods. In this paper, we present how to use this type of in- formation within a statistical machine translation system. We show that it is possible to significantly decrease train- ing and test corpus perplexity of the translation models. In addition, we per- form a rescoring of � -Best lists us- ing our maximum entropy model and thereby yield an improvement in trans- lation quality. Experimental results are presented on the so-called “Verbmobil Task”. 1 Introduction Typically, the lexicon models used in statistical machine translation systems are only single-word based, that is one word in the source language cor- responds to only one word in the target language. Those lexicon models lack from context infor- mation that can be extracted from the same paral- lel corpus. This additional information could be: � Simple context information: information of the words surrounding the word pair; � Syntactic information: part-of-speech in- formation, syntactic constituent, sentence mood; � Semantic information: disambiguation in- formation (e.g. from WordNet), cur- rent/previous speech or dialog act. To include this additional information within the statistical framework we use the maximum en- tropy approach. This approach has been applied in natural language processing to a variety of tasks. (Berger et al., 1996) applies this approach to the so-called IBM Candide system to build con- text dependent models, compute automatic sen- tence splitting and to improve word reordering in translation. Similar techniques are used in (Pap- ineni et al., 1996; Papineni et al., 1998) for so- called direct translation models instead of those proposed in (Brown et al., 1993). (Foster, 2000) describes two methods for incorporating informa- tion about the relative position of bilingual word pairs into a maximum entropy translation model. Other authors have applied this approach to lan- guage modeling (Rosenfeld, 1996; Martin et al., 1999; Peters and Klakow, 1999). A short review of the maximum entropy approach is outlined in Section 3. 2 Statistical Machine Translation The goal of the translation process in statisti- cal machine translation can be formulated as fol- lows: A source language string �� �� � �� � � is to be translated into a target language string  � � �� . In the experiments reported in this paper, the source language is German and the target language is English. …

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