Incorporating Structural Alignment Biases into an Attentional Neural Translation Model (original) (raw)

Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. How- ever their modelling formulation is overly simplistic, and omits several key inductive bi- ases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agree- ment over translation directions. We show im- provements over a baseline attentional model and standard phrase-based model over sev- eral language pairs, evaluating on difficult lan- guages in a low resource setting.

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