Datasets:

maximoss
/

gqnli-fr

Tasks:

Text Classification

Modalities:

Formats:

Sub-tasks:

natural-language-inference

multi-input-text-classification

Languages:

Size:

Libraries:

License:

Dataset card Data Studio Files Files and versions Community

maximoss commited on Jun 13, 2023

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@@ -108,7 +108,7 @@ This repository contains a French version of the [GQNLI](https://github.com/ruix
 ### Citation Information
-```
 @inproceedings{cui-etal-2022-generalized-quantifiers,
     title = "Generalized Quantifiers as a Source of Error in Multilingual {NLU} Benchmarks",
     author = "Cui, Ruixiang  and
@@ -124,7 +124,7 @@ This repository contains a French version of the [GQNLI](https://github.com/ruix
     pages = "4875--4893",
     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.",
 }
-```
 ### Contributions

 ### Citation Information
+````BibTeX
 @inproceedings{cui-etal-2022-generalized-quantifiers,
     title = "Generalized Quantifiers as a Source of Error in Multilingual {NLU} Benchmarks",
     author = "Cui, Ruixiang  and
     pages = "4875--4893",
     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.",
 }
+````
 ### Contributions