Topic adaptation for lecture translation through bilingual latent semantic models
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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The major limitation in bilingual latent semantic analysis (bLSA) is the requirement of parallel training corpora. Motivated by semi-supervised learning, we propose a clusterbased bLSA training approach to incorporate monolingual corpora. Treating each parallel document pair as centroids of the parallel document clusters, each monolingual document is associated to the closest centroid according to their topic similarity. The resulting parallel document clusters are used as constraints to enforce a one-to-one topic correspondence in variational EM. Slight performance improvement in crosslingual language model adaptation is observed compared to the baseline without monolingual corpora.