@inproceedings{wu-etal-2023-improving,
title = "Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning",
author = "Wu, Zhanglin and
Li, Zongyao and
Wei, Daimeng and
Shang, Hengchao and
Guo, Jiaxin and
Chen, Xiaoyu and
Rao, Zhiqiang and
Yu, Zhengzhe and
Yang, Jinlong and
Li, Shaojun and
Xie, Yuhao and
Wei, Bin and
Zheng, Jiawei and
Zhu, Ming and
Lei, Lizhi and
Yang, Hao and
Jiang, Yanfei",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.iwslt-1.13",
doi = "10.18653/v1/2023.iwslt-1.13",
pages = "180--186",
abstract = "This paper presents Huawei Translation Service Center (HW-TSC){'}s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions. We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results. Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability. According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level. Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and reranking-based transductive learning methods to improve the formality control capability of the model.",
}
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<abstract>This paper presents Huawei Translation Service Center (HW-TSC)’s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions. We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results. Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability. According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level. Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and reranking-based transductive learning methods to improve the formality control capability of the model.</abstract>
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%0 Conference Proceedings
%T Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning
%A Wu, Zhanglin
%A Li, Zongyao
%A Wei, Daimeng
%A Shang, Hengchao
%A Guo, Jiaxin
%A Chen, Xiaoyu
%A Rao, Zhiqiang
%A Yu, Zhengzhe
%A Yang, Jinlong
%A Li, Shaojun
%A Xie, Yuhao
%A Wei, Bin
%A Zheng, Jiawei
%A Zhu, Ming
%A Lei, Lizhi
%A Yang, Hao
%A Jiang, Yanfei
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada (in-person and online)
%F wu-etal-2023-improving
%X This paper presents Huawei Translation Service Center (HW-TSC)’s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions. We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results. Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability. According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level. Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and reranking-based transductive learning methods to improve the formality control capability of the model.
%R 10.18653/v1/2023.iwslt-1.13
%U https://aclanthology.org/2023.iwslt-1.13
%U https://doi.org/10.18653/v1/2023.iwslt-1.13
%P 180-186
Markdown (Informal)
[Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning](https://aclanthology.org/2023.iwslt-1.13) (Wu et al., IWSLT 2023)
ACL
- Zhanglin Wu, Zongyao Li, Daimeng Wei, Hengchao Shang, Jiaxin Guo, Xiaoyu Chen, Zhiqiang Rao, Zhengzhe Yu, Jinlong Yang, Shaojun Li, Yuhao Xie, Bin Wei, Jiawei Zheng, Ming Zhu, Lizhi Lei, Hao Yang, and Yanfei Jiang. 2023. Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning. In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), pages 180–186, Toronto, Canada (in-person and online). Association for Computational Linguistics.