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    <abstract>This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming. We revisit CTC for machine translation and demonstrate that a simple CTC model can achieve state-of-the-art for single-step non-autoregressive machine translation, contrary to what prior work indicates. In addition, we adapt the Imputer model for non-autoregressive machine translation and demonstrate that Imputer with just 4 generation steps can match the performance of an autoregressive Transformer baseline. Our latent alignment models are simpler than many existing non-autoregressive translation baselines; for example, we do not require target length prediction or re-scoring with an autoregressive model. On the competitive WMT‘14 En\rightarrowDe task, our CTC model achieves 25.7 BLEU with a single generation step, while Imputer achieves 27.5 BLEU with 2 generation steps, and 28.0 BLEU with 4 generation steps. This compares favourably to the autoregressive Transformer baseline at 27.8 BLEU.</abstract>
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%0 Conference Proceedings
%T Non-Autoregressive Machine Translation with Latent Alignments
%A Saharia, Chitwan
%A Chan, William
%A Saxena, Saurabh
%A Norouzi, Mohammad
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F saharia-etal-2020-non
%X This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming. We revisit CTC for machine translation and demonstrate that a simple CTC model can achieve state-of-the-art for single-step non-autoregressive machine translation, contrary to what prior work indicates. In addition, we adapt the Imputer model for non-autoregressive machine translation and demonstrate that Imputer with just 4 generation steps can match the performance of an autoregressive Transformer baseline. Our latent alignment models are simpler than many existing non-autoregressive translation baselines; for example, we do not require target length prediction or re-scoring with an autoregressive model. On the competitive WMT‘14 En\rightarrowDe task, our CTC model achieves 25.7 BLEU with a single generation step, while Imputer achieves 27.5 BLEU with 2 generation steps, and 28.0 BLEU with 4 generation steps. This compares favourably to the autoregressive Transformer baseline at 27.8 BLEU.
%R 10.18653/v1/2020.emnlp-main.83
%U https://aclanthology.org/2020.emnlp-main.83/
%U https://doi.org/10.18653/v1/2020.emnlp-main.83
%P 1098-1108

Markdown (Informal)

[Non-Autoregressive Machine Translation with Latent Alignments](https://aclanthology.org/2020.emnlp-main.83/) (Saharia et al., EMNLP 2020)

• Non-Autoregressive Machine Translation with Latent Alignments (Saharia et al., EMNLP 2020)

ACL

• Chitwan Saharia, William Chan, Saurabh Saxena, and Mohammad Norouzi. 2020. Non-Autoregressive Machine Translation with Latent Alignments. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1098–1108, Online. Association for Computational Linguistics.