@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.",
}

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.
Wu, Zhanglin Li, Zongyao Wei, Daimeng Shang, Hengchao Guo, Jiaxin Chen, Xiaoyu Rao, Zhiqiang Yu, Zhengzhe Yang, Jinlong Li, Shaojun Xie, Yuhao Wei, Bin Zheng, Jiawei Zhu, Ming Lei, Lizhi Yang, Hao Jiang, Yanfei
Improving Neural Machine Translation Formality Control with Domain Adaptation and Reranking-based Transductive Learning