metadata

license: mit
task_categories:
  - question-answering
  - text-retrieval
language:
  - fr
tags:
  - education
size_categories:
  - 10K<n<100K

Alloprof dataset

This is the dataset refered to in our paper:

Alloprof: a new French question-answer education dataset and its use in an information retrieval case study (https://arxiv.org/abs/2302.07738)

This dataset was provided by AlloProf, an organisation in Quebec, Canada offering resources and a help forum curated by a large number of teachers to students on all subjects taught from in primary and secondary school.

Raw data on questions is available in the following files:

data/questions/categories.json: subjects and their corresponding id
data/questions/comments.json: explanation (answer) data
data/questions/discussions.json: question data
data/questions/grades.json: grades and their corresponding id
data/questions/roles.json: information about the user type for each user id

Raw data on reference pages is available in the following files:

data/pages/page-content-en.json: data for the reference pages in English
data/pages/page-content-fr.json: data for the reference pages in French

The data can be parsed and structured using the script scripts/parse_data.py to create the file data/alloprof.csv with the following columns:

id (str) : Id of the document
url (str) : URL of the document
text (str) : Parsed text of the document
language (str) : Either "fr" or "en", the language of the document
user (int) : Id corresponding to the user who asked the question
images (str) : ";" separated list of URLs of images contained in the document
relevant (str) : ";" separated list of document ids appearing as links in the explanation to that document. For files, this will always be empty as there are no corresponding explanation
is_query (bool) : If this document is a question
subject (str) : ";" separated list of school subjects the document is related to
grade (str) : ";" separated list of school grade levels the document is related to
possible (str) : ";" separated list of possible documents ids this document may refer to. This list corresponds to every document of the same subject and grade. For files, this will always be empty to speed up reading and writing

The possible column depends on arguments passed to the scripts to add related subjects, and lower and higher grade levels to the possible documents (see paper). Also note that the provided alloprof.csv file is stored with git lfs and can be pulled with git lfs install && git lfs pull.

For images, a script to download them is available as scripts/download_images.py.

If you have any questions, don't hesitate to mail us at antoine.lefebvre-brossard@mila.quebec.

Please cite our work as:

@misc{lef23,
  doi = {10.48550/ARXIV.2302.07738},
  url = {https://arxiv.org/abs/2302.07738},
  author = {Lefebvre-Brossard, Antoine and Gazaille, Stephane and Desmarais, Michel C.},
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Alloprof: a new French question-answer education dataset and its use in an information retrieval case study},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}