Abstract
This study proposes the bilingual segmented topic model (BiSTM), which hierarchically models documents by treating each document as a set of segments, e.g., sections. While previous bilingual topic models, such as bilingual latent Dirichlet allocation (BiLDA) (Mimno et al., 2009; Ni et al., 2009), consider only cross-lingual alignments between entire documents, the proposed model considers cross-lingual alignments between segments in addition to document-level alignments and assigns the same topic distribution to aligned segments. This study also presents a method for simultaneously inferring latent topics and segmentation boundaries, incorporating unsupervised topic segmentation (Du et al., 2013) into BiSTM. Experimental results show that the proposed model significantly out-performs BiLDA in terms of perplexity and demonstrates improved performance in translation pair extraction (up to +0.083 extraction accuracy).