Using Language Models to Disambiguate Lexical Choices in Translation

• URL: http://arxiv.org/abs/2411.05781v1
• Date: Fri, 08 Nov 2024 18:48:57 GMT
• Title: Using Language Models to Disambiguate Lexical Choices in Translation
• Authors: Josh Barua, Sanjay Subramanian, Kayo Yin, Alane Suhr,
• Abstract summary: In translation, a concept represented by a single word in a source language can have multiple variations in a target language. We work with native speakers of nine languages to create DTAiLS, a dataset of 1,377 sentence pairs that exhibit cross-lingual concept variation when translating from English.
• Score: 13.795280427753648
• License:
• Abstract: In translation, a concept represented by a single word in a source language can have multiple variations in a target language. The task of lexical selection requires using context to identify which variation is most appropriate for a source text. We work with native speakers of nine languages to create DTAiLS, a dataset of 1,377 sentence pairs that exhibit cross-lingual concept variation when translating from English. We evaluate recent LLMs and neural machine translation systems on DTAiLS, with the best-performing model, GPT-4, achieving from 67 to 85% accuracy across languages. Finally, we use language models to generate English rules describing target-language concept variations. Providing weaker models with high-quality lexical rules improves accuracy substantially, in some cases reaching or outperforming GPT-4.

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