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        <title>What Have We Achieved on Non-autoregressive Translation?</title>
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    <abstract>Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncertainty about the true proximity of NAT to AT. To address this gap, we systematically evaluate four representative NAT methods across various dimensions, including human evaluation. Our empirical results demonstrate that despite narrowing the performance gap, state-of-the-art NAT still underperforms AT under more reliable evaluation metrics. Furthermore, we discover that explicitly modeling dependencies is crucial for generating natural language and generalizing to out-of-distribution sequences.</abstract>
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%0 Conference Proceedings
%T What Have We Achieved on Non-autoregressive Translation?
%A Li, Yafu
%A Zhang, Huajian
%A Yan, Jianhao
%A Yin, Yongjing
%A Zhang, Yue
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-achieved
%X Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncertainty about the true proximity of NAT to AT. To address this gap, we systematically evaluate four representative NAT methods across various dimensions, including human evaluation. Our empirical results demonstrate that despite narrowing the performance gap, state-of-the-art NAT still underperforms AT under more reliable evaluation metrics. Furthermore, we discover that explicitly modeling dependencies is crucial for generating natural language and generalizing to out-of-distribution sequences.
%R 10.18653/v1/2024.findings-acl.452
%U https://aclanthology.org/2024.findings-acl.452
%U https://doi.org/10.18653/v1/2024.findings-acl.452
%P 7585-7606

Markdown (Informal)

[What Have We Achieved on Non-autoregressive Translation?](https://aclanthology.org/2024.findings-acl.452) (Li et al., Findings 2024)

• What Have We Achieved on Non-autoregressive Translation? (Li et al., Findings 2024)

ACL

• Yafu Li, Huajian Zhang, Jianhao Yan, Yongjing Yin, and Yue Zhang. 2024. What Have We Achieved on Non-autoregressive Translation?. In Findings of the Association for Computational Linguistics: ACL 2024, pages 7585–7606, Bangkok, Thailand. Association for Computational Linguistics.