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README.md
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The sentences in the dataset were drawn from all the existing French Natural Language Inference (NLI) datasets, namely DACCORD, FraCaS-FR, GQNLI-FR, RTE3-FR (dev and test), SICK-FR, and XNLI (dev and test). These datasets are structured as sentence pairs (premise(s) and hypothesis) annotated with a gold label (entailment, neutral, or contradiction).
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The resulting FOLLIE dataset preserves all these elements, in addition to providing logical representations of the sentences. It can therefore be used either for natural language–to–FOL translation tasks or for NLI/RTE tasks using neurosymbolic approaches (i.e., solving NLI by reasoning over FOL formulas).
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The logical formulas follow the format used in the NLTK package, thus
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- **Curated by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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The sentences in the dataset were drawn from all the existing French Natural Language Inference (NLI) datasets, namely DACCORD, FraCaS-FR, GQNLI-FR, RTE3-FR (dev and test), SICK-FR, and XNLI (dev and test). These datasets are structured as sentence pairs (premise(s) and hypothesis) annotated with a gold label (entailment, neutral, or contradiction).
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The resulting FOLLIE dataset preserves all these elements, in addition to providing logical representations of the sentences. It can therefore be used either for natural language–to–FOL translation tasks or for NLI/RTE tasks using neurosymbolic approaches (i.e., solving NLI by reasoning over FOL formulas).
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The logical formulas follow the format used in the NLTK package, thus they are fully compatible with the NLTK Logic Parser.
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- **Curated by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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