| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - fr |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-classification |
| - text-generation |
| task_ids: |
| - parsing |
| - natural-language-inference |
| - language-modeling |
| --- |
| |
| # Dataset Card for FOLLIE dataset |
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| <!-- Provide a quick summary of the dataset. --> |
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| ## Dataset Details |
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| ### Dataset Description |
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| <!-- Provide a longer summary of what this dataset is. --> |
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| FOLLIE (First-Order Logic for Language Inference and Entailment) is the first dataset for French with natural language sentences and their corresponding first-order logic (FOL) formulas. |
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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). |
| 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] |
| - **Funded by [optional]:** [More Information Needed] |
| - **Shared by [optional]:** [More Information Needed] |
| - **Language(s) (NLP):** [More Information Needed] |
| - **License:** [More Information Needed] |
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| ### Dataset Sources [optional] |
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| <!-- Provide the basic links for the dataset. --> |
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| - **Repository:** [More Information Needed] |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** [More Information Needed] |
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| ## Uses |
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| <!-- Address questions around how the dataset is intended to be used. --> |
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| ### Direct Use |
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| <!-- This section describes suitable use cases for the dataset. --> |
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| [More Information Needed] |
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| ### Out-of-Scope Use |
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| <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> |
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| [More Information Needed] |
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| ## Dataset Structure |
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| <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
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| ### Data Fields |
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| - `problem_id`: An identifier for each sentence1--sentence2 pair. |
| - `p1` - `p5`: The FOL formulas corresponding to the premise (or premises, if many) of the original French source datasets. |
| - `p1_nl` - `p5_nl`: The premise (or premises, if many) in natural language (French) from the original French source datasets. |
| - `h1` - `h3`: The FOL formulas corresponding to the hypothesis (or hypotheses, if many) of the original French source datasets. |
| - `h1_nl` - `h3_nl`: The hypothesis (or hypotheses, if many) in natural language (French) from the original French source datasets. |
| - `label`: The classification label, with possible values `yes` (for entailment), `no` (for contradiction), `unknown` (for neutral), or `undef` (only for some FraCaS examples, when it is not clear). |
| - `dataset`: The source dataset (DACCORD, FraCaS, GQNLI, RTE3, SICK, XNLI). |
| - `subset`: The subset (TEST, TRAIL, TRAIN) in which each example is in its original source dataset. |
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| ## Dataset Creation |
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| ### Curation Rationale |
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| <!-- Motivation for the creation of this dataset. --> |
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| [More Information Needed] |
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| ### Source Data |
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| <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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| #### Data Collection and Processing |
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| <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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| [More Information Needed] |
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| #### Who are the source data producers? |
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| <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
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| [More Information Needed] |
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| ### Annotations [optional] |
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| <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
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| #### Annotation process |
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| <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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| [More Information Needed] |
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| #### Who are the annotators? |
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| <!-- This section describes the people or systems who created the annotations. --> |
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| [More Information Needed] |
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| #### Personal and Sensitive Information |
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| <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> |
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| [More Information Needed] |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| [More Information Needed] |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| ````BibTeX |
| @inproceedings{skandalis-etal-2025-neurosymbolic, |
| title = "Neurosymbolic {AI} for Natural Language Inference in {F}rench : combining {LLM}s and theorem provers for semantic parsing and natural language reasoning", |
| author = "Skandalis, Maximos and |
| Abzianidze, Lasha and |
| Moot, Richard and |
| Retor{\'e}, Christian and |
| Robillard, Simon", |
| editor = "Evang, Kilian and |
| Kallmeyer, Laura and |
| Pogodalla, Sylvain", |
| booktitle = "Proceedings of the 16th International Conference on Computational Semantics", |
| month = sep, |
| year = "2025", |
| address = {D{\"u}sseldorf, Germany}, |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2025.iwcs-main.21/", |
| pages = "242--253", |
| ISBN = "979-8-89176-316-6", |
| abstract = "In this article, we describe the first comprehensive neurosymbolic pipeline for the task of Natural Language Inference (NLI) for French, with the synergy of Large Language Models (CamemBERT) and automated theorem provers (GrailLight, LangPro). LLMs prepare the input for GrailLight by tagging each token with Part-of-Speech and grammatical information based on the Type-Logical Grammar formalism. GrailLight then produces the lambda-terms given as input to the LangPro theorem prover, a tableau-based theorem prover for natural logic originally developped for English. Currently, the proposed system works on the French version of SICK dataset. The results obtained are comparable to the ones on the English and Dutch versions of SICK with the same LangPro theorem prover, and are better than the results of recent transformers on this specific dataset.Finally, we have identified ways to further improve the results obtained, such as giving access to the theorem prover to lexical knowledge via a knowledge base for French." |
| } |
| ```` |
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| **ACL:** |
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| Maximos Skandalis, Lasha Abzianidze, Richard Moot, Christian Retoré, and Simon Robillard. 2025. [Neurosymbolic AI for Natural Language Inference in French : combining LLMs and theorem provers for semantic parsing and natural language reasoning](https://aclanthology.org/2025.iwcs-main.21). In *Proceedings of the 16th International Conference on Computational Semantics*, pages 242–253, Düsseldorf, Germany. Association for Computational Linguistics. |
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| ### Acknowledgements |
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| This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France. |
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| ## Dataset Card Authors [optional] |
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| [More Information Needed] |
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| ## Dataset Card Contact |
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| [More Information Needed] |
