We show that semantic relationships can be used to improve word alignment, in addition to the lexical and syntactic features that are typically used. In this paper, we present a method based on a neural network to automatically derive word similarity from monolingual data. We present an extension to word alignment models that exploits word similarity. Our experiments, in both large-scale and resourcelimited settings, show improvements in word alignment tasks as well as translation tasks.
REF proposed a method using word similarity from monolingual corpora to improve machine translation.
Improving Word Alignment using Word Similarity
EMNLP
Computer Science