Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks
Ruixiang Cui, Daniel Hershcovich, Anders Søgaard · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 languageindependent 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 directlytargeted 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.