SCOI：用于机器翻译的语法增强覆盖度上下文示例选择
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
AI总结：
本文提出语法增强覆盖度上下文示例选择策略（SCOI），通过结合深层句法覆盖度与词汇覆盖度为机器翻译选择更优的上下文示例，在六个翻译方向上取得所有无学习方法中最高的平均COMET分数。
摘要：
上下文学习（ICL）极大地提升了大型语言模型（LLMs）在各类下游任务上的性能，其改进高度依赖于示例的质量。在本研究中，我们引入句法知识为机器翻译（MT）选择更好的上下文示例。我们提出了一种新策略，即语法增强覆盖度上下文示例选择（SCOI），利用超越传统词匹配的深层句法结构。具体而言，我们借助简化的树到多项式算法计算多项式项的覆盖度来衡量集合级句法覆盖度，并使用词汇重叠衡量词汇覆盖度。此外，我们设计了一种交替选择方法来结合两种覆盖度指标，充分利用句法和词汇信息。我们使用两个多语言LLMs在六个翻译方向上进行了实验。实证结果表明，我们提出的SCOI在所有无学习方法中获得了最高的平均COMET分数，表明结合句法和词汇覆盖度成功有助于为MT选择更好的上下文示例。代码可在 https://github.com/JamyDon/SCOI 获取。
英文摘要：
In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demonstrations. In this work, we introduce syntactic knowledge to select better in-context examples for machine translation (MT). We propose a new strategy, namely Syntax-augmented COverage-based In-context example selection (SCOI), leveraging the deep syntactic structure beyond conventional word matching. Specifically, we measure the set-level syntactic coverage by computing the coverage of polynomial terms with the help of a simplified tree-to-polynomial algorithm, and lexical coverage using word overlap. Furthermore, we devise an alternate selection approach to combine both coverage measures, taking advantage of syntactic and lexical information. We conduct experiments with two multi-lingual LLMs on six translation directions. Empirical results show that our proposed SCOI obtains the highest average COMET score among all learning-free methods, indicating that combining syntactic and lexical coverage successfully helps to select better in-context examples for MT. Our code is available at https://github.com/JamyDon/SCOI.
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。
