@inproceedings{li-etal-2022-prompt,
    title = "Prompt-Driven Neural Machine Translation",
    author = "Li, Yafu  and
      Yin, Yongjing  and
      Li, Jing  and
      Zhang, Yue",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.findings-acl.203/",
    doi = "10.18653/v1/2022.findings-acl.203",
    pages = "2579--2590",
    abstract = "Neural machine translation (NMT) has obtained significant performance improvement over the recent years. However, NMT models still face various challenges including fragility and lack of style flexibility. Moreover, current methods for instance-level constraints are limited in that they are either constraint-specific or model-specific. To this end, we propose prompt-driven neural machine translation to incorporate prompts for enhancing translation control and enriching flexibility. Empirical results demonstrate the effectiveness of our method in both prompt responding and translation quality. Through human evaluation, we further show the flexibility of prompt control and the efficiency in human-in-the-loop translation."
}