DÉfi Fouille de Textes (DEFT) 2023 @ TALN 2023

DEFT 2023: French Pharmacy Exam Multiple-Choice Question Answering

The workshop is based around the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain called FrenchMedMCQA. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.

Dataset Description

	Links
Homepage:	https://github.com/qanastek/DEFT-2023
Repository:	https://github.com/qanastek/DEFT-2023
Paper:	https://hal.archives-ouvertes.fr/hal-03824241v1
Leaderboard:	Coming Soon
Point of Contact:	deft-2023@listes.univ-avignon.fr

Dataset Summary

FrenchMedMCQA is the first open-source Multiple-Choice Question Answering (MCQA) dataset which propose questions with a single or multiple answer(s) based on real-world medical exam questions.

Languages

The questions and answers are available in French.

Dataset Structure

Data Instances

Example of a question with multiple answers.

In JSON format:

{
    "id": "6979d46501a3270436d37b98cf351439fbcbec8d5890d293dabfb8f85f723904",
    "question": "Cocher la (les) proposition(s) exacte(s)\u00a0: Le m\u00e9tronidazole\u00a0:",
    "answers": {
        "a": "Est un d\u00e9riv\u00e9 du pyrazole",
        "b": "Peut induire un effet antabuse",
        "c": "Peut \u00eatre administr\u00e9 par voie parent\u00e9rale intraveineuse",
        "d": "Peut \u00eatre utilis\u00e9 dans certaines parasitoses \u00e0 protozoaires",
        "e": "Est inefficace dans les infections \u00e0 germes ana\u00e9robies"
    },
    "correct_answers": [
        "b",
        "c",
        "d"
    ],
    "subject_name": "pharmacie",
    "type": "multiple",
    "nbr_correct_answers": 3
}

In TSV format:

id	question	answers.a	answers.b	answers.c	answers.d	answers.e	correct_answers	nbr_correct_answers
6979d46501a3270436d37b98cf351439fbcbec8d5890d293dabfb8f85f723904	Cocher la (les) proposition(s) exacte(s) : Le métronidazole :	Est un dérivé du pyrazole	Peut induire un effet antabuse	Peut être administré par voie parentérale intraveineuse	Peut être utilisé dans certaines parasitoses à protozoaires	Est inefficace dans les infections à germes anaérobies	b|c|d	3

Data Fields

• id : Question identifier
• question : Question text
• answers : The list of 5 options from A to E
• correct_answers : Correct option(s) (Can be one or more answer(s) to the question)
• subject_name : Medical field treated by the question
• type : The type of question is single (one correct answer only) or multiple (multiple correct answers)
• nbr_correct_answers : The number of correct answers

Data Splits

Globally, 70 % of the questions are kept for the train, 10 % for validation and the last 20 % for testing.

The distribution of the subsets according to the number of answers are as follow :

# Answers	Training	Validation	Total
1	595	164	1,080
2	528	45	670
3	718	71	930
4	296	30	382
5	34	2	43
Total	2171	312	3,105

Bootstrapping training scripts

Architecture

We are providing a basic bootstrapping script for training, which use Transformers library to train a CamemBERT-base model to predict the correct(s) answer(s) to the question by modeling it as a multi-label problem.

P.-S.: It's not the training script of the reference, and had the only purpose to make easier to start with the tasks.

How to use the training scripts (each file header contains the full procedure):

• Change the path of the HuggingFace dataset to your local or remote location.
  • My local path: dataset_base = load_dataset("/users/ylabrak/DEFT-2023/Data/Huggingface/DEFT-2023/DEFT2023.py")
  • Using HuggingFace Hub (required to send an invitation to DEFT-2023 organization or your HuggingFace username to the workshop email): dataset_base = load_dataset("DEFT-2023/DEFT2023", use_auth_token=True)
• Run the training script:
  • python TrainFrenchMedMCQA-CLASSIFICATION-Full.py --model_name="camembert-base"
  • python TrainFrenchMedMCQA-QA.py --model_name="camembert-base"

Evaluation scripts

Run the evaluation script:

• Classification: python EvaluationClassification.py --references="./references_classification.txt" --predictions="./submission-fsd94g9gds49g94sd9gs9g.txt"
• MCQA: python EvaluationQA.py --references="./references_qa.txt" --predictions="./submission-MCQA-fsd94g9gds49g94sd9gs9g.txt"

Where the predictions argument of the scripts is the submission file generated at the end of the training scripts and which follow the formats:

Classification

1187fa6bffd499eb4XXc90679d7fbca822d62bc639d1b9c94c68ae20e46f6004;5
u787c8575bb93274XXa27b7b51f7ea797802157bf3b0e985f62b9164a2ec3287;2
5me98b574405e91313XX337b65e76ce9c8bee4837790d7ad189f63fc40a70899;1

MCQA

1187fa6bffd499eb4XXc90679d7fbca822d62bc639d1b9c94c68ae20e46f6004;A|B
u787c8575bb93274XXa27b7b51f7ea797802157bf3b0e985f62b9164a2ec3287;A|B|C|D|E
5me98b574405e91313XX337b65e76ce9c8bee4837790d7ad189f63fc40a70899;E

Citation Information

We would very much appreciate it if you cite our publications :

@inproceedings{labrak-etal-2022-frenchmedmcqa,
    title = "{F}rench{M}ed{MCQA}: A {F}rench Multiple-Choice Question Answering Dataset for Medical domain",
    author = "Labrak, Yanis  and
      Bazoge, Adrien  and
      Dufour, Richard  and
      Daille, Beatrice  and
      Gourraud, Pierre-Antoine  and
      Morin, Emmanuel  and
      Rouvier, Mickael",
    booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.louhi-1.5",
    pages = "41--46",
    abstract = "This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
}