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 resource-limited settings, show improvements in word alignment tasks as well as translation tasks.
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