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        <title>Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering</title>
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        <namePart type="given">Seungjun</namePart>
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            <title>Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering
%A Lee, Seungjun
%A Moon, Hyeonseok
%A Park, Chanjun
%A Lim, Heuiseok
%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 lee-etal-2023-improving-formality
%X 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.
%R 10.18653/v1/2023.iwslt-1.40
%U https://aclanthology.org/2023.iwslt-1.40
%U https://doi.org/10.18653/v1/2023.iwslt-1.40
%P 420-432

Markdown (Informal)

[Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering](https://aclanthology.org/2023.iwslt-1.40) (Lee et al., IWSLT 2023)

• Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering (Lee et al., IWSLT 2023)

ACL

• Seungjun Lee, Hyeonseok Moon, Chanjun Park, and Heuiseok Lim. 2023. Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering. In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), pages 420–432, Toronto, Canada (in-person and online). Association for Computational Linguistics.