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        <title>Neural Machine Translation with Source-Side Latent Graph Parsing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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    <abstract>This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1012</identifier>
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%0 Conference Proceedings
%T Neural Machine Translation with Source-Side Latent Graph Parsing
%A Hashimoto, Kazuma
%A Tsuruoka, Yoshimasa
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F hashimoto-tsuruoka-2017-neural
%X This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.
%R 10.18653/v1/D17-1012
%U https://aclanthology.org/D17-1012/
%U https://doi.org/10.18653/v1/D17-1012
%P 125-135

Markdown (Informal)

[Neural Machine Translation with Source-Side Latent Graph Parsing](https://aclanthology.org/D17-1012/) (Hashimoto & Tsuruoka, EMNLP 2017)

• Neural Machine Translation with Source-Side Latent Graph Parsing (Hashimoto & Tsuruoka, EMNLP 2017)

ACL

• Kazuma Hashimoto and Yoshimasa Tsuruoka. 2017. Neural Machine Translation with Source-Side Latent Graph Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 125–135, Copenhagen, Denmark. Association for Computational Linguistics.