Polylingual tree-based topic models for translation domain adaptation

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.