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.
@article{arxiv.2306.14514,
title = {Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation},
author = {Seugnjun Lee and Hyeonseok Moon and Chanjun Park and Heuiseok Lim},
journal= {arXiv preprint arXiv:2306.14514},
year = {2023}
}
Comments
Accepted for Data-centric Machine Learning Research (DMLR) Workshop at ICML 2023