Topic Adaptation for Machine Translation of E-Commerce Content

We describe effort to improve machine translation of item titles found in a large e-commerce inventory through topic modeling and adaptation. Item titles are short texts which typically contain brand names that do not have to be translated, and item attributes whose translation often depends on the context. Both issues call for robust methods to integrate context infor- mation in the machine translation process in order to reduce translation ambiguity. We survey both existing topic adaptation approaches and propose novel methods that augment the standard phrase-table models with sparse features and dense features measuring the topic match between each phrase-pair and the input text. We report extensive experiments on the translation of item titles from English into Brazilian Portuguese, and show the impact of topic adaptation both with and without domain adaptation.