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arXiv · 2306.14514

Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation

Seugnjun Lee·Hyeonseok Moon·Chanjun Park·Heuiseok Lim
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Abstract

In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data handling, and 2) synthetic data generation using large-scale language models and empirical prompt engineering. This approach demonstrates a considerable improvement over the baseline, highlighting the effectiveness of data-centric techniques. Our prompt engineering strategy further improves performance by producing superior synthetic translation examples.

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Seugnjun Lee, Hyeonseok Moon, Chanjun Park, Heuiseok Lim. 2023-06-26. Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation. https://arxiv.org/abs/2306.14514

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