NLP research on aligning lexical representation spaces to one another has so far focused on aligning language spaces in their entirety. However, cognitive science has long focused on a local perspective, investigating whether translation equivalents truly share the same meaning or the extent that cultural and regional influences result in meaning variations. With recent technological advances and the increasing amounts of available data, the longstanding question of cross-lingual lexical alignment can now be approached in a more data-driven manner. However, developing metrics for the task requires some methodology for comparing metric efficacy. We address this gap and present a methodology for analyzing both synthetic validations and a novel naturalistic validation using lexical gaps in the kinship domain. We further propose new metrics, hitherto unexplored on this task, based on contextualized embeddings. Our analysis spans 16 diverse languages, demonstrating that there is substantial room for improvement with the use of newer language models. Our research paves the way for more accurate and nuanced cross-lingual lexical alignment methodologies and evaluation.

本研究解决了现有跨语言词汇对齐方法中对整体语言空间对齐的关注不足，转而探讨地方和文化差异对词汇意义的影响。通过提出新的基于上下文嵌入的度量，并进行合成和自然验证，本研究揭示了新语言模型在词汇对齐中的潜力，促进了更加准确的跨语言词汇对齐方法的发展。

局部测量跨语言词汇对齐：从领域和词级的视角