局部测量跨语言词汇对齐：领域与词级视角
Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspective
AI总结：
本文从局部视角研究跨语言词汇对齐，提出基于上下文嵌入的新指标及合成与自然验证方法，分析16种语言表明现有模型仍有改进空间。
摘要：
将词汇表示空间相互对齐的NLP研究迄今侧重于整体对齐语言空间。然而，认知科学长期关注局部视角，研究翻译等价词是否真正共享相同含义，或文化与区域影响在多大程度上导致意义变化。随着近期技术进步和可用数据量增加，跨语言词汇对齐这一长期问题现在可以更数据驱动的方式处理。但为该任务开发指标需要比较指标有效性的方法。我们填补这一空白，提出一种方法，分析合成验证及使用亲属领域词汇空缺的新型自然验证。我们进一步提出基于上下文嵌入的新指标，该指标此前未在此任务探索。我们的分析涵盖16种不同语言，表明使用较新语言模型仍有很大改进空间。我们的研究为更准确细致的跨语言词汇对齐方法与评估铺平道路。
英文摘要：
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.
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。
