Polylingual Tree-Based Topic Models for Translation Domain Adaptation

Yuening Hu, Ke Zhai, Vladimir Eidelman, Jordan L. Boyd-Graber

Abstract: Topic models, an unsupervised technique for inferring translation domains improve machine translation quality. However, previous work uses only the source language and completely ignores the target language, which can disambiguate domains. We propose new polylingual tree-based topic models to extract domain knowledge that considers both source and target languages and derive three different inference schemes. We evaluate our model on a Chinese to English translation task and obtain up to 1.2 BLEU improvement over strong baselines.