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In this paper, we introduce a data-driven approach for Formality-SensitiveMachine Translation (FSMT) that caters to the unique linguistic properties offour target languages. Our methodology centers on two core strategies: 1)language-specific data handling, and 2) synthetic data generation usinglarge-scale language models and empirical prompt engineering. This approachdemonstrates a considerable improvement over the baseline, highlighting theeffectiveness of data-centric techniques. Our prompt engineering strategyfurther improves performance by producing superior synthetic translationexamples.

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