@inproceedings{jiang-etal-2021-learning,
title = "Learning Kernel-Smoothed Machine Translation with Retrieved Examples",
author = "Jiang, Qingnan and
Wang, Mingxuan and
Cao, Jun and
Cheng, Shanbo and
Huang, Shujian and
Li, Lei",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.579",
doi = "10.18653/v1/2021.emnlp-main.579",
pages = "7280--7290",
abstract = "How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining? Despite the great success of neural machine translation, updating the deployed models online remains a challenge. Existing non-parametric approaches that retrieve similar examples from a database to guide the translation process are promising but are prone to overfit the retrieved examples. However, non-parametric methods are prone to overfit the retrieved examples. In this work, we propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER), an effective approach to adapt neural machine translation models online. Experiments on domain adaptation and multi-domain machine translation datasets show that even without expensive retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over the best existing online adaptation methods. The code and trained models are released at \url{https://github.com/jiangqn/KSTER}.",
}
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<abstract>How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining? Despite the great success of neural machine translation, updating the deployed models online remains a challenge. Existing non-parametric approaches that retrieve similar examples from a database to guide the translation process are promising but are prone to overfit the retrieved examples. However, non-parametric methods are prone to overfit the retrieved examples. In this work, we propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER), an effective approach to adapt neural machine translation models online. Experiments on domain adaptation and multi-domain machine translation datasets show that even without expensive retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over the best existing online adaptation methods. The code and trained models are released at https://github.com/jiangqn/KSTER.</abstract>
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%0 Conference Proceedings
%T Learning Kernel-Smoothed Machine Translation with Retrieved Examples
%A Jiang, Qingnan
%A Wang, Mingxuan
%A Cao, Jun
%A Cheng, Shanbo
%A Huang, Shujian
%A Li, Lei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F jiang-etal-2021-learning
%X How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining? Despite the great success of neural machine translation, updating the deployed models online remains a challenge. Existing non-parametric approaches that retrieve similar examples from a database to guide the translation process are promising but are prone to overfit the retrieved examples. However, non-parametric methods are prone to overfit the retrieved examples. In this work, we propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER), an effective approach to adapt neural machine translation models online. Experiments on domain adaptation and multi-domain machine translation datasets show that even without expensive retraining, KSTER is able to achieve improvement of 1.1 to 1.5 BLEU scores over the best existing online adaptation methods. The code and trained models are released at https://github.com/jiangqn/KSTER.
%R 10.18653/v1/2021.emnlp-main.579
%U https://aclanthology.org/2021.emnlp-main.579
%U https://doi.org/10.18653/v1/2021.emnlp-main.579
%P 7280-7290
Markdown (Informal)
[Learning Kernel-Smoothed Machine Translation with Retrieved Examples](https://aclanthology.org/2021.emnlp-main.579) (Jiang et al., EMNLP 2021)
ACL
- Qingnan Jiang, Mingxuan Wang, Jun Cao, Shanbo Cheng, Shujian Huang, and Lei Li. 2021. Learning Kernel-Smoothed Machine Translation with Retrieved Examples. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7280–7290, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.