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arXiv · 1905.06596

Joint Source-Target Self Attention with Locality Constraints

José A. R. Fonollosa·Noe Casas·Marta R. Costa-jussà
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Abstract

The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequences. In this paper we study a new architecture that breaks with both conventions. Our simplified architecture consists in the decoder part of a transformer model, based on self-attention, but with locality constraints applied on the attention receptive field. As input for training, both source and target sentences are fed to the network, which is trained as a language model. At inference time, the target tokens are predicted autoregressively starting with the source sequence as previous tokens. The proposed model achieves a new state of the art of 35.7 BLEU on IWSLT'14 German-English and matches the best reported results in the literature on the WMT'14 English-German and WMT'14 English-French translation benchmarks.

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José A. R. Fonollosa, Noe Casas, Marta R. Costa-jussà. 2019-05-16. Joint Source-Target Self Attention with Locality Constraints. https://arxiv.org/abs/1905.06596

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