Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs

摘要

We examine the employment of word em-beddings for machine translation (MT) of phrasal verbs (PVs), a linguistic phenomenon with challenging semantics. Using word embeddings, we augment the translation model with two features: one modelling distributional semantic properties of the source and target phrase and another modelling the degree of compo-sitionality of PVs. We also obtain paraphrases to increase the amount of relevant training data. Our method leads to improved translation quality for PVs in a case study with English to Bulgarian MT system.