Paper: Topic Models for Dynamic Translation Model Adaptation

ACL ID P12-2023
Title Topic Models for Dynamic Translation Model Adaptation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

We propose an approach that biases machine translation systems toward relevant transla- tions based on topic-specific contexts, where topics are induced in an unsupervised way using topic models; this can be thought of as inducing subcorpora for adaptation with- out any human annotation. We use these topic distributions to compute topic-dependent lex- ical weighting probabilities and directly in- corporate them into our translation model as features. Conditioning lexical probabilities on the topic biases translations toward topic- relevant output, resulting in significant im- provements of up to 1 BLEU and 3 TER on Chinese to English translation over a strong baseline.