Dynamic Topic Adaptation for SMT using Distributional Profiles
Eva Hasler, Barry Haddow, Philipp Koehn · 2014
Despite its potential to improve lexical selection, most state-of-the-art machine translation systems take only minimal contextual information into account.We capture context with a topic model over distributional profiles built from the context words of each translation unit.Topic distributions are inferred for each translation unit and used to adapt the translation model dynamically to a given test context by measuring their similarity.We show that combining information from both local and global test contexts helps to improve lexical selection and outperforms a baseline system by up to 1.15 BLEU.We test our topic-adapted model on a diverse data set containing documents from three different domains and achieve competitive performance in comparison with two supervised domain-adapted systems.