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        <title>Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks</title>
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        <namePart type="given">Ruixiang</namePart>
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            <namePart type="given">Marie-Catherine</namePart>
            <namePart type="family">de Marneffe</namePart>
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            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
            <namePart type="family">Meza Ruiz</namePart>
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    <abstract>Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today’s NLU models still struggle to capture their semantics. We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models. We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops. Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages. To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.</abstract>
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    <identifier type="doi">10.18653/v1/2022.naacl-main.359</identifier>
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        <date>2022-07</date>
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%0 Conference Proceedings
%T Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks
%A Cui, Ruixiang
%A Hershcovich, Daniel
%A Søgaard, Anders
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F cui-etal-2022-generalized-quantifiers
%X Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today’s NLU models still struggle to capture their semantics. We rely on Generalized Quantifier Theory for language-independent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models. We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops. Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages. To facilitate directly-targeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.
%R 10.18653/v1/2022.naacl-main.359
%U https://aclanthology.org/2022.naacl-main.359
%U https://doi.org/10.18653/v1/2022.naacl-main.359
%P 4875-4893

Markdown (Informal)

[Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://aclanthology.org/2022.naacl-main.359) (Cui et al., NAACL 2022)

• Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks (Cui et al., NAACL 2022)

ACL

• Ruixiang Cui, Daniel Hershcovich, and Anders Søgaard. 2022. Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4875–4893, Seattle, United States. Association for Computational Linguistics.