Paper: Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation

ACL ID C10-1138
Title Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

In this paper we investigate the challenges of applying statistical machine translation to meeting conversations, with a particu- lar view towards analyzing the importance of modeling contextual factors such as the larger discourse context and topic/domain information on translation performance. We describe the collection of a small cor- pus of parallel meeting data, the develop- ment of a statistical machine translation system in the absence of genre-matched training data, and we present a quantita- tive analysis of translation errors result- ing from the lack of contextual modeling inherent in standard statistical machine translation systems. Finally, we demon- strate how the largest source of translation errors (lack of topic/domain knowledge) can be addressed by applying document- leve...