Paper: Polylingual Tree-Based Topic Models for Translation Domain Adaptation

ACL ID P14-1110
Title Polylingual Tree-Based Topic Models for Translation Domain Adaptation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Topic models, an unsupervised technique for inferring translation domains improve machine translation quality. However, pre- vious work uses only the source language and completely ignores the target language, which can disambiguate domains. We pro- pose new polylingual tree-based topic mod- els to extract domain knowledge that con- siders both source and target languages and derive three different inference schemes. We evaluate our model on a Chinese to En- glish translation task and obtain up to 1.2 BLEU improvement over strong baselines.