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        <title>Learning to Parse and Translate Improves Neural Machine Translation</title>
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    <abstract>There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.</abstract>
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%0 Conference Proceedings
%T Learning to Parse and Translate Improves Neural Machine Translation
%A Eriguchi, Akiko
%A Tsuruoka, Yoshimasa
%A Cho, Kyunghyun
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F eriguchi-etal-2017-learning
%X There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.
%R 10.18653/v1/P17-2012
%U https://aclanthology.org/P17-2012
%U https://doi.org/10.18653/v1/P17-2012
%P 72-78

Markdown (Informal)

[Learning to Parse and Translate Improves Neural Machine Translation](https://aclanthology.org/P17-2012) (Eriguchi et al., ACL 2017)

• Learning to Parse and Translate Improves Neural Machine Translation (Eriguchi et al., ACL 2017)

ACL

• Akiko Eriguchi, Yoshimasa Tsuruoka, and Kyunghyun Cho. 2017. Learning to Parse and Translate Improves Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 72–78, Vancouver, Canada. Association for Computational Linguistics.