This work presents a simplified approach to bilingual topic modeling for language model adaptation by combining text in the source and target language into very short documents and performing Probabilistic Latent Semantic Analysis (PLSA) during model training. During inference, documents containing only the source language can be used to infer a full topic-word distribution on all words in the target language’s vocabulary, from which we perform Minimum Discrimination Information (MDI) adaptation on a background language model (LM). We apply our approach on the English-French IWSLT 2010 TED Talk exercise, and report a 15% reduction in perplexity and relative BLEU and NIST improvements of 3% and 2.4%, respectively over a baseline only using a 5-gram background LM over the entire translation task. Our topic modeling approach is simpler to construct than its counterparts.

Topic Adaptation for Lecture Translation through Bilingual Latent Semantic Models

Federico, Marcello
2011

Abstract

This work presents a simplified approach to bilingual topic modeling for language model adaptation by combining text in the source and target language into very short documents and performing Probabilistic Latent Semantic Analysis (PLSA) during model training. During inference, documents containing only the source language can be used to infer a full topic-word distribution on all words in the target language’s vocabulary, from which we perform Minimum Discrimination Information (MDI) adaptation on a background language model (LM). We apply our approach on the English-French IWSLT 2010 TED Talk exercise, and report a 15% reduction in perplexity and relative BLEU and NIST improvements of 3% and 2.4%, respectively over a baseline only using a 5-gram background LM over the entire translation task. Our topic modeling approach is simpler to construct than its counterparts.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/41985
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