Work

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

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2306.14514

Authors: Lee, Seugnjun, Moon, Hyeonseok, Park, Chanjun, Lim, Heuiseok

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Awards: G4700831490, G4992080425, G5198230845, G7537039438, G7796477359

OpenAlex ID: W4382490444

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