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        <title>Semi-Autoregressive Neural Machine Translation</title>
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        <namePart type="given">Chunqi</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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    <abstract>Existing approaches to neural machine translation are typically autoregressive models. While these models attain state-of-the-art translation quality, they are suffering from low parallelizability and thus slow at decoding long sequences. In this paper, we propose a novel model for fast sequence generation — the semi-autoregressive Transformer (SAT). The SAT keeps the autoregressive property in global but relieves in local and thus are able to produce multiple successive words in parallel at each time step. Experiments conducted on English-German and Chinese-English translation tasks show that the SAT achieves a good balance between translation quality and decoding speed. On WMT’14 English-German translation, the SAT achieves 5.58\times speedup while maintaining 88% translation quality, significantly better than the previous non-autoregressive methods. When produces two words at each time step, the SAT is almost lossless (only 1% degeneration in BLEU score).</abstract>
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    <identifier type="doi">10.18653/v1/D18-1044</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Semi-Autoregressive Neural Machine Translation
%A Wang, Chunqi
%A Zhang, Ji
%A Chen, Haiqing
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wang-etal-2018-semi-autoregressive
%X Existing approaches to neural machine translation are typically autoregressive models. While these models attain state-of-the-art translation quality, they are suffering from low parallelizability and thus slow at decoding long sequences. In this paper, we propose a novel model for fast sequence generation — the semi-autoregressive Transformer (SAT). The SAT keeps the autoregressive property in global but relieves in local and thus are able to produce multiple successive words in parallel at each time step. Experiments conducted on English-German and Chinese-English translation tasks show that the SAT achieves a good balance between translation quality and decoding speed. On WMT’14 English-German translation, the SAT achieves 5.58\times speedup while maintaining 88% translation quality, significantly better than the previous non-autoregressive methods. When produces two words at each time step, the SAT is almost lossless (only 1% degeneration in BLEU score).
%R 10.18653/v1/D18-1044
%U https://aclanthology.org/D18-1044/
%U https://doi.org/10.18653/v1/D18-1044
%P 479-488

Markdown (Informal)

[Semi-Autoregressive Neural Machine Translation](https://aclanthology.org/D18-1044/) (Wang et al., EMNLP 2018)

• Semi-Autoregressive Neural Machine Translation (Wang et al., EMNLP 2018)

ACL

• Chunqi Wang, Ji Zhang, and Haiqing Chen. 2018. Semi-Autoregressive Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 479–488, Brussels, Belgium. Association for Computational Linguistics.