Paper: Refined Lexicon Models For Statistical Machine Translation Using A Maximum Entropy Approach

ACL ID P01-1027
Title Refined Lexicon Models For Statistical Machine Translation Using A Maximum Entropy Approach
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001
Authors

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 a2 -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”.