← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1818 · 记录 #1
False Friends or Cognates? A Cross-lingual Semantic Ambiguity Evaluation for Galician, Portuguese and Spanish
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:48
摘要
The linguistic proximity between Galician, Portuguese, and Spanish results in a lexical overlap that often conceals semantic interference. This is particularly evident in false friends, posing a challenge for NLP systems.In this work, we assess whether state-of-the-art language models can identify and process false friends among these languages. We introduce six cross-lingual datasets –created manually or using semi-automatic methods, with all instances being carefully verified– covering cognates and false friends. We evaluate a broad range of encoder and decoder models of varying sizes via zero-shot and few-shot settings. Our results highlight the challenging nature of the task, but also show the clear progress made by LLMs in recent years, particularly those of a larger size, with smaller language models struggling on the task. Notably, unlike other tasks where language distance poses additional challenges, we find that linguistic proximity itself introduces errors: closely related language pairs tend to perform worse, reflecting the challenge of semantic discrimination due to lexical overlap.
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记录 #1 · 2026-07 · 最新观测版本
False Friends or Cognates? A Cross-lingual Semantic Ambiguity Evaluation for Galician, Portuguese and Spanish
The linguistic proximity between Galician, Portuguese, and Spanish results in a lexical overlap that often conceals semantic interference. This is particularly evident in false friends, posing a challenge for NLP systems.In this work, we assess whether state-of-the-art language models can identify and process false friends among these languages. We introduce six cross-lingual datasets –created manually or using semi-automatic methods, with all instances being carefully verified– covering cognates and false friends. We evaluate a broad range of encoder and decoder models of varying sizes via zero-shot and few-shot settings. Our results highlight the challenging nature of the task, but also show the clear progress made by LLMs in recent years, particularly those of a larger size, with smaller language models struggling on the task. Notably, unlike other tasks where language distance poses additional challenges, we find that linguistic proximity itself introduces errors: closely related language pairs tend to perform worse, reflecting the challenge of semantic discrimination due to lexical overlap.
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