Incremental Topic-Based Translation Model Adaptation for Conversational Spoken Language Translation

Sanjika Hewavitharana, Dennis Mehay, Sankaranarayanan Ananthakrishnan and Prem Natarajan

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

We describe a translation model adaptation approach for conversational spoken language translation (CSLT), which encourages the use of contextually appropriate translation options from relevant training conversations. Our approach employs a monolingual LDA topic model to derive a similarity measure between the the test conversation and the set of training conversations, which is used to bias translation choices towards the current context. A significant novelty of our adaptation technique is its incremental nature; we continuously update the topic distribution on the evolving test conversation as new utterances become available. Thus, our approach is well-suited to the causal constraint of spoken conversations. On an English-to-Iraqi CSLT task, the proposed approach gives significant improvements over a baseline system as measured by BLEU, TER, and NIST. Interestingly, the incremental approach outperforms a non-incremental oracle that has up-front knowledge of the whole conversation.

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