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Incorporating Structural Alignment Biases into an Attentional Neural Translation Model

Domaine:

natural language processing

Type de record:

paper
Créateur:
CohHoaVymYao
Hôte:avatar
Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases 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 agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting. 10 pages

Visit

arxiv.org

Tasks

machine translation

Tags

Computation and Language

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