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    <abstract>Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality. However, training these models to achieve high quality while maintaining low latency often leads to a tendency for aggressive anticipation. We argue that such issue stems from the autoregressive architecture upon which most existing SiMT models are built. To address those issues, we propose non-autoregressive streaming Transformer (NAST) which comprises a unidirectional encoder and a non-autoregressive decoder with intra-chunk parallelism. We enable NAST to generate the blank token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and train it to maximize the non-monotonic latent alignment with an alignment-based latency loss. Experiments on various SiMT benchmarks demonstrate that NAST outperforms previous strong autoregressive SiMT baselines.</abstract>
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%0 Conference Proceedings
%T Non-autoregressive Streaming Transformer for Simultaneous Translation
%A Ma, Zhengrui
%A Zhang, Shaolei
%A Guo, Shoutao
%A Shao, Chenze
%A Zhang, Min
%A Feng, Yang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ma-etal-2023-non
%X Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality. However, training these models to achieve high quality while maintaining low latency often leads to a tendency for aggressive anticipation. We argue that such issue stems from the autoregressive architecture upon which most existing SiMT models are built. To address those issues, we propose non-autoregressive streaming Transformer (NAST) which comprises a unidirectional encoder and a non-autoregressive decoder with intra-chunk parallelism. We enable NAST to generate the blank token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and train it to maximize the non-monotonic latent alignment with an alignment-based latency loss. Experiments on various SiMT benchmarks demonstrate that NAST outperforms previous strong autoregressive SiMT baselines.
%R 10.18653/v1/2023.emnlp-main.314
%U https://aclanthology.org/2023.emnlp-main.314
%U https://doi.org/10.18653/v1/2023.emnlp-main.314
%P 5177-5190

Markdown (Informal)

[Non-autoregressive Streaming Transformer for Simultaneous Translation](https://aclanthology.org/2023.emnlp-main.314) (Ma et al., EMNLP 2023)

• Non-autoregressive Streaming Transformer for Simultaneous Translation (Ma et al., EMNLP 2023)

ACL

• Zhengrui Ma, Shaolei Zhang, Shoutao Guo, Chenze Shao, Min Zhang, and Yang Feng. 2023. Non-autoregressive Streaming Transformer for Simultaneous Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5177–5190, Singapore. Association for Computational Linguistics.