@inproceedings{cui-etal-2022-generalized,
    title = "Generalized Quantifiers as a Source of Error in Multilingual {NLU} Benchmarks",
    author = "Cui, Ruixiang  and
      Hershcovich, Daniel  and
      S{\o}gaard, Anders",
    editor = "Bartolo, Max  and
      Kirk, Hannah  and
      Rodriguez, Pedro  and
      Margatina, Katerina  and
      Thrush, Tristan  and
      Jia, Robin  and
      Stenetorp, Pontus  and
      Williams, Adina  and
      Kiela, Douwe",
    booktitle = "Proceedings of the First Workshop on Dynamic Adversarial Data Collection",
    month = jul,
    year = "2022",
    address = "Seattle, WA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.dadc-1.7/",
    doi = "10.18653/v1/2022.dadc-1.7",
    pages = "61--61",
    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."
}