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        <title>AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference</title>
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        <namePart type="given">Yuewei</namePart>
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    <name type="personal">
        <namePart type="given">Chaitanya</namePart>
        <namePart type="family">Malaviya</namePart>
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    <name type="personal">
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.75</identifier>
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%0 Conference Proceedings
%T AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference
%A Yuan, Yuewei
%A Malaviya, Chaitanya
%A Yatskar, Mark
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yuan-etal-2023-ambicoref
%X 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.
%R 10.18653/v1/2023.findings-eacl.75
%U https://aclanthology.org/2023.findings-eacl.75
%U https://doi.org/10.18653/v1/2023.findings-eacl.75
%P 1023-1030

Markdown (Informal)

[AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference](https://aclanthology.org/2023.findings-eacl.75) (Yuan et al., Findings 2023)

• AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference (Yuan et al., Findings 2023)

ACL

• Yuewei Yuan, Chaitanya Malaviya, and Mark Yatskar. 2023. AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1023–1030, Dubrovnik, Croatia. Association for Computational Linguistics.