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---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- question-answering
task_ids:
- natural-language-inference
- multi-input-text-classification
language:
- fr
- en
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:** https://gitlab.inria.fr/semagramme-public-projects/resources/french-fracas
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This repository contains the French version of the FraCaS Test Suite introduced in [this paper](https://aclanthology.org/2020.lrec-1.721.pdf), as well as the original English one, in a TSV format (as opposed to the XML format provided with the original paper).
FraCaS stands for "Framework for Computational Semantics".
### Supported Tasks and Leaderboards
This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.
It can also be used for the task of Question Answering (QA) (when using the columns `question` and `answer` instead of `hypothesis` and `label`, respectively).
## Dataset Structure
### Data Fields
- `id`: Index number.
- `premises`: All premises provided for this particular example, concatenated, in French.
- `hypothesis`: The translated hypothesis in the target language (French).
- `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`), or undef (for undefined).
- `question`: The hypothesis in the form of question, in French.
- `answer`: The answer to the question, with possible values `Yes` (0), `Don't know` / `Unknown` (1), `No` (2), `undef`, or a longer phrase containing qualifications or elaborations such as `Yes, on one reading`.
- `premises_original`: All premises provided for this particular example, concatenated, in their language of origin (English).
- `premise1`: The first premise in English.
- `premise1_original`: The first premise in English.
- `premise2`: When available, the second premise in French.
- `premise2_original`: When available, the second premise in English.
- `premise3`: When available, the third premise in French.
- `premise3_original`: When available, the third premise in English.
- `premise4`: When available, the fourth premise in French.
- `premise4_original`: When available, the fourth premise in English.
- `premise5`: When available, the fifth premise in French.
- `premise5_original`: When available, the fifth premise in English.
- `hypothesis_original`: The hypothesis in English.
- `question_original`: The hypothesis in the form of question, in English.
- `note`: Text from the source document intended to explain or justify the answer, or notes added to a number of problems in order to explain issues which arose during translation.
- `topic`: Problem set / topic.
### Data Splits
The premise counts are distributed as follows:
| # premises |# problems|% problems|
|---------:|------:|------------:|
| 1 | 192 | 55.5 % |
| 2 | 122 | 35.3 % |
| 3 | 29 | 8.4 % |
| 4 | 2 | 0.6 % |
| 5 | 1 | 0.3 % |
The answer distribution is roughly as follows:
| # problems |Percentage|Answer|
|---------:|------:|------------:|
| 180 | 52% | Yes |
| 94 | 27% | Don't know |
| 31 | 9% | No |
| 41 | 12% | [other / complex] |
Here's the breakdown by topic:
| sec | topic| start| count|%|single-premise|
|-------------|--:|--:|--:|--:|--:|
| 1 |Quantifiers|1|80|23 %|50|
| 2 |Plurals|81|33|10 %|24|
| 3 |Anaphora|114|28| 8 %|6|
| 4 |Ellipsis|142|55|16 %|25|
| 5 |Adjectives|197|23|7 %|15|
| 6 |Comparatives|220|31|9 %|16|
| 7 |Temporal|251|75|22 %|39|
| 8 |Verbs|326|8|2 %|8|
| 9 |Attitudes|334|4|10 %|9|
## Additional Information
### Citation Information
**BibTeX:**
````BibTeX
@inproceedings{amblard-etal-2020-french,
title = "A {F}rench Version of the {F}ra{C}a{S} Test Suite",
author = "Amblard, Maxime and
Beysson, Cl{\'e}ment and
de Groote, Philippe and
Guillaume, Bruno and
Pogodalla, Sylvain",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.721",
pages = "5887--5895",
abstract = "This paper presents a French version of the FraCaS test suite. This test suite, originally written in English, contains problems illustrating semantic inference in natural language. We describe linguistic choices we had to make when translating the FraCaS test suite in French, and discuss some of the issues that were raised by the translation. We also report an experiment we ran in order to test both the translation and the logical semantics underlying the problems of the test suite. This provides a way of checking formal semanticists{'} hypotheses against actual semantic capacity of speakers (in the present case, French speakers), and allow us to compare the results we obtained with the ones of similar experiments that have been conducted for other languages.",
language = "English",
ISBN = "979-10-95546-34-4",
}
````
**ACL:**
Maxime Amblard, Clément Beysson, Philippe de Groote, Bruno Guillaume, and Sylvain Pogodalla. 2020. [A French Version of the FraCaS Test Suite](https://aclanthology.org/2020.lrec-1.721/). In *Proceedings of the Twelfth Language Resources and Evaluation Conference*, pages 5887–5895, Marseille, France. European Language Resources Association. |