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Mutual Information and Diverse Decoding Improve Neural Machine Translation

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      Abstract

      Sequence-to-sequence neural translation models learn semantic and syntactic relations between sentence pairs by optimizing the likelihood of the target given the source, i.e., \(p(y|x)\), an objective that ignores other potentially useful sources of information. We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets. We implement the model with a simple re-ranking method, and also introduce a decoding algorithm that increases diversity in the N-best list produced by the first pass. Applied to the WMT German/English and French/English tasks, the proposed models offers a consistent performance boost on both standard LSTM and attention-based neural MT architectures.

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      Journal
      2016-01-03
      2016-03-22
      1601.00372

      http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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      cs.CL cs.AI

      Theoretical computer science, Artificial intelligence

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