@inproceedings{zhao-etal-2023-adaptive,
title = "Adaptive Policy with Wait-k Model for Simultaneous Translation",
author = "Zhao, Libo and
Fan, Kai and
Luo, Wei and
Jing, Wu and
Wang, Shushu and
Zeng, Ziqian and
Huang, Zhongqiang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.293",
doi = "10.18653/v1/2023.emnlp-main.293",
pages = "4816--4832",
abstract = "Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-k policy coupled with a standalone wait-k translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-k model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-k model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.",
}
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<abstract>Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-k policy coupled with a standalone wait-k translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-k model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-k model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.</abstract>
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%0 Conference Proceedings
%T Adaptive Policy with Wait-k Model for Simultaneous Translation
%A Zhao, Libo
%A Fan, Kai
%A Luo, Wei
%A Jing, Wu
%A Wang, Shushu
%A Zeng, Ziqian
%A Huang, Zhongqiang
%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 zhao-etal-2023-adaptive
%X Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-k policy coupled with a standalone wait-k translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-k model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-k model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.
%R 10.18653/v1/2023.emnlp-main.293
%U https://aclanthology.org/2023.emnlp-main.293
%U https://doi.org/10.18653/v1/2023.emnlp-main.293
%P 4816-4832
Markdown (Informal)
[Adaptive Policy with Wait-k Model for Simultaneous Translation](https://aclanthology.org/2023.emnlp-main.293) (Zhao et al., EMNLP 2023)
ACL
- Libo Zhao, Kai Fan, Wei Luo, Wu Jing, Shushu Wang, Ziqian Zeng, and Zhongqiang Huang. 2023. Adaptive Policy with Wait-k Model for Simultaneous Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4816–4832, Singapore. Association for Computational Linguistics.