@article {12420,
	title = {Topic Models for Dynamic Translation Model Adaptation},
	journal = {Association for Computational Linguistics},
	year = {2012},
	month = {2012///},
	abstract = {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.},
	author = {Vladimir Eidelman and Jordan Boyd-Graber and Resnik, Philip}
}
