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 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.

Date

2013

Authors

Sanjika Hewavitharana, Dennis Mehay, Sankaranarayanan Ananthakrishnan, Prem Natarajan

Conference

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Pages

697-701