@inproceedings{lee-etal-2023-improving-formality,
    title = "Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering",
    author = "Lee, Seungjun  and
      Moon, Hyeonseok  and
      Park, Chanjun  and
      Lim, Heuiseok",
    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-1.40",
    doi = "10.18653/v1/2023.iwslt-1.40",
    pages = "420--432",
    abstract = "In this paper, we present the KU x Upstage team{'}s submission for the Special Task on Formality Control on Spoken Language Translation, which involves translating English into four languages with diverse grammatical formality markers. Our methodology comprises two primary components: 1) a language-specific data-driven approach, and 2) the generation of synthetic data through the employment of large-scale language models and empirically-grounded prompt engineering. By adapting methodologies and models to accommodate the unique linguistic properties of each language, we observe a notable enhancement in performance relative to the baseline, substantiating the heightened efficacy of data-driven approaches. Moreover, our devised prompt engineering strategy yields superior synthetic translation instances.",
}

In this paper, we present the KU x Upstage team{'}s submission for the Special Task on Formality Control on Spoken Language Translation, which involves translating English into four languages with diverse grammatical formality markers. Our methodology comprises two primary components: 1) a language-specific data-driven approach, and 2) the generation of synthetic data through the employment of large-scale language models and empirically-grounded prompt engineering. By adapting methodologies and models to accommodate the unique linguistic properties of each language, we observe a notable enhancement in performance relative to the baseline, substantiating the heightened efficacy of data-driven approaches. Moreover, our devised prompt engineering strategy yields superior synthetic translation instances.
Lee, Seungjun Moon, Hyeonseok Park, Chanjun Lim, Heuiseok
Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering