Bilingual Segmented Topic Model

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 outperforms BiLDA in terms of perplexity and demonstrates improved performance in translation pair extraction (up to +0.083 extraction accuracy).