Efficient Terminology Integration for LLM-based Translation in Specialized Domains

• URL: http://arxiv.org/abs/2410.15690v1
• Date: Mon, 21 Oct 2024 07:01:25 GMT
• Title: Efficient Terminology Integration for LLM-based Translation in Specialized Domains
• Authors: Sejoon Kim, Mingi Sung, Jeonghwan Lee, Hyunkuk Lim, Jorge Froilan Gimenez Perez,
• Abstract summary: In specialized fields such as patent, finance, or biomedical domains, terminology is crucial for translation. We introduce a methodology that efficiently trains models with a smaller amount of data while preserving the accuracy of terminology translation. This methodology enhances the model's ability to handle specialized terminology and ensures high-quality translations.
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• Abstract: Traditional machine translation methods typically involve training models directly on large parallel corpora, with limited emphasis on specialized terminology. However, In specialized fields such as patent, finance, or biomedical domains, terminology is crucial for translation, with many terms that needs to be translated following agreed-upon conventions. In this paper we introduce a methodology that efficiently trains models with a smaller amount of data while preserving the accuracy of terminology translation. We achieve this through a systematic process of term extraction and glossary creation using the Trie Tree algorithm, followed by data reconstruction to teach the LLM how to integrate these specialized terms. This methodology enhances the model's ability to handle specialized terminology and ensures high-quality translations, particularly in fields where term consistency is crucial. Our approach has demonstrated exceptional performance, achieving the highest translation score among participants in the WMT patent task to date, showcasing its effectiveness and broad applicability in specialized translation domains where general methods often fall short.

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