使用语言模型消解翻译中的词汇选择歧义
Using Language Models to Disambiguate Lexical Choices in Translation
AI总结：
本研究创建跨语言词汇变异数据集DTAiLS，评估LLM与NMT系统，并利用语言模型生成词汇规则，显著提升较弱模型的翻译准确率，部分场景超越GPT-4。
摘要：
在翻译中，源语言中由单个单词表示的概念在目标语言中可能有多种变体。词汇选择任务要求利用上下文来确定哪种变体最适合源文本。我们与九种语言的母语者合作，创建了DTAiLS数据集，该数据集包含1,377对句子，展示了从英语翻译时出现的跨语言概念变异。我们在DTAiLS上评估了最新的LLM和神经机器翻译系统，其中表现最佳的模型GPT-4在不同语言上的准确率在67%至85%之间。最后，我们使用语言模型生成描述目标语言概念变异的英语规则。为较弱的模型提供高质量的词汇规则可显著提高准确率，在某些情况下达到或超过GPT-4的水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
