Word Sense Disambiguation Improves Statistical Machine Translation

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 33–40, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Word Sense Disambiguation Improves Statistical Machine Translation Yee Seng Chan and Hwee Tou Ng Department of Computer Science National University of Singapore 3 Science Drive 2 Singapore 117543 {chanys, nght}@comp.nus.edu.sg David Chiang Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292, USA chiang@isi.edu Abstract Recent research presents conflicting evi- dence on whether word sense disambigua- tion (WSD) systems can help to improve the performance of statistical machine transla- tion (MT) systems. In this paper, we suc- cessfully integrate a state-of-the-art WSD system into a state-of-the-art hierarchical phrase-based MT system, Hiero. We show for the first time that integrating a WSD sys- tem improves the performance of a state-of- the-art statistical MT system on an actual translation task. Furthermore, the improve- ment is statistically significant. 1 Introduction Many words have multiple meanings, depending on the context in which they are used. Word sense dis- ambiguation (WSD) is the task of determining the correct meaning or sense of a word in context. WSD is regarded as an important research problem and is assumed to be helpful for applications such as ma- chine translation (MT) and information retrieval. In translation, different senses of a word w in a source language may have different translations in a target language, depending on the particular mean- ing of w in context. Hence, the assumption is that in resolving sense ambiguity, a WSD system will be able to help an MT system to determine the correct translation for an ambiguous word. To determine the correct sense of a word, WSD systems typically use a wide array of features that are not limited to the lo- cal context of w, and some of these features may not be used by state-of-the-art statistical MT systems. To perform translation, state-of-the-art MT sys- tems use a statistical phrase-based approach (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004) by treating phrases as the basic units of translation. In this approach, a phrase can be any sequence of consecutive words and is not nec- essarily linguistically meaningful. Capitalizing on the strength of the phrase-based approach, Chiang (2005) introduced a hierarchical phrase-based sta- tistical MT system, Hiero, which achieves signifi- cantly better translation performance than Pharaoh (Koehn, 2004a), which is a state-of-the-art phrase- based statistical MT system. Recently, some researchers investigated whether performing WSD will help to improve the perfor- mance of an MT system. Carpuat and Wu (2005) integrated the translation predictions from a Chinese WSD system (Carpuat et al., 2004) into a Chinese- English word-based statistical MT system using the ISI ReWrite decoder (Germann, 2003). Though they acknowledged that directly using English transla- tions as word senses would be ideal, they instead predicted the HowNet sense of a word and then used the English gloss of the HowNet sense as the WSD model’s predicted translation. They …

Content truncated to 500 words