AmbiCoref:评估人类和模型对模糊共指的敏感性

Yuewei Yuan, Chaitanya Malaviya, Mark Yatskar
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引用次数: 4

摘要

如果有一句话“艾比告诉布里特尼她惹恼了考特尼”,人们会很难理解“她”指的是谁,并要求澄清。然而,如果“心烦意乱”一词被替换为“拥抱”,“她”毫不含糊地指的是艾比。我们研究了现代共指消解模型是否对这种代词歧义敏感。为此,我们构建了AmbiCoref,这是一个由具有歧义和不歧义指称的最小句子对组成的诊断语料库。我们的例子概括了心理语言学对人类对动词及其论据的特定排列的歧义感知的研究。分析表明:(1)在歧义的AmbiCoref例子中,人类对指称的把握不如不歧义的,(2)大多数共指称模型在歧义对和不歧义对之间的输出几乎没有差异。我们发布了AmbiCoref作为一个诊断语料库,用于测试模型是否像对待人类一样对待歧义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference
Given a sentence “Abby told Brittney that she upset Courtney”, one would struggle to understand who “she” refers to, and ask for clarification. However, if the word “upset” were replaced with “hugged”, “she” unambiguously refers to Abby. We study if modern coreference resolution models are sensitive to such pronominal ambiguity. To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents. Our examples generalize psycholinguistic studies of human perception of ambiguity around particular arrangements of verbs and their arguments. Analysis shows that (1) humans are less sure of referents in ambiguous AmbiCoref examples than unambiguous ones, and (2) most coreference models show little difference in output between ambiguous and unambiguous pairs. We release AmbiCoref as a diagnostic corpus for testing whether models treat ambiguity similarly to humans.
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