Adjunct Alignment in Translation Data with an Application to Phrase-Based Statistical Machine Translation

Enriching statistical models with linguistic knowledge has been a major concern in Machine Translation (MT). In monolingual data, adjuncts are optional constituents contributing secondarily to the meaning of a sentence. One can therefore hypothesize that this secondary status is preserved in translation, and thus that adjuncts may align consistently with their adjunct translations, suggesting they form optional phrase pairs in parallel corpora. In this paper we verify this hypothesis on French-English translation data, and explore the utility of compiling adjunct-poor data for augmenting the training data of a phrase-based machine translation model.