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README.md

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+# CQuAE: A New French Question-Answering Corpus for Teaching Assistant
+
+CQuAE (Corpus of QUestions for Assisting Education) is a French question-answering dataset in the domain of secondary education.
+It has been designed to facilitate the development of virtual teaching assistants,
+with a particular focus on creating and answering complex questions that go beyond simple fact extraction.
+CQuAE includes questions, answers, and corresponding source documents (excerpts of textbook or Wikipedia articles).
+By providing both straightforward and deeper, multi-sentence, or interpretative queries, the dataset supports diverse QA tasks, including factual, definitional, explanatory, and synthetic question types.
+
+This dataset was described in:
+“CQuAE : Un nouveau corpus de question-réponse pour l’enseignement”  
+by Thomas Gerald, Louis Tamames, Sofiane Ettayeb, Patrick Paroubek, Anne Vilnat.
+
+----------------------------------------------------------------------------------------------------
+
+## Table of Contents
+
+1. [Dataset Summary](#dataset-summary)  
+2. [Supported Tasks](#supported-tasks)  
+3. [Dataset Structure](#dataset-structure)  
+4. [Data Fields](#data-fields)  
+5. [Versions Summary](#versions-summary)  
+6. [Source Data and Construction](#source-data-and-construction)  
+7. [Annotation Process and Types of Questions](#annotation-process-and-types-of-questions)  
+8. [Applications and Examples](#applications-and-examples)  
+9. [Citation](#citation)
+
+----------------------------------------------------------------------------------------------------
+
+## Dataset Summary
+
+CQuAE is designed to train and evaluate QA systems capable of handling a range of question types in French.
+Questions are grounded in educational material from various subject areas—mainly history, geography, and sciences—at the late middle-school and early high-school levels.
+Each entry comprises:
+
+• A manually written question (French).  
+• The corresponding source document excerpt(s).  
+• A manually written answer (in French).  
+• The question’s type (factual, definition, course-level explanatory, or synthetic).  
+• Metadata such as a question identifier and document title(s).
+
+One of the key goals behind CQuAE is to collect and evaluate questions that require varying levels of reasoning complexity.
+While many QA datasets in French emphasize short factual or named-entity answers, CQuAE includes longer, more elaborate responses that often span multiple elements of a text.
+
+----------------------------------------------------------------------------------------------------
+
+## Supported Tasks
+
+• **Question Answering (QA)**: Given a question and a relevant document, generate or extract an answer.  
+• **Complex QA**: Some questions require multi-sentence answers, synthesis, or deeper interpretation.  
+• **Document Retrieval (RAG)**: Identify the relevant passages in the larger corpus to answer a question.
+
+----------------------------------------------------------------------------------------------------
+
+## Dataset Structure
+
+The dataset is organized as follows (feature schema applies to all splits):
+
+• **train_v1**: 10,431 examples.  
+  - First version of the training data.  
+
+• **train_v2**: 7,156 examples.  
+  - A partially “human-filtered” or corrected version of the training data (some problematic instances from v1 were filtered or improved).
+
+• **eval**: 512 examples.  
+  - Evaluation split for model development.  
+
+• **test**: 512 examples.  
+  - Standard test set.  
+
+• **test_top1**: 512 examples.  
+  - Same underlying question set as “test,” except that the single document provided here was retrieved automatically from the full collection via a retrieval-augmented generation (RAG) approach. In other words, it may differ from the original reference document used by annotators.
+
+A high-level representation of the dataset structure:
+
+----------------------------------------------------------------------------------------------------
+
+## Data Fields
+
+Each split contains the following fields:
+
+• **question** (string): The question in French.  
+• **title** (string): Source title (Chapter of the textbook or wikipedia article).  
+• **documents** (list): The list of text excerpts used by the annotator to create the question and its answer.  
+• **type** (string): The type of question. Possible values include:  
+  - “factuelle” (factual)  
+  - “définition” (definition)  
+  - “cours” (explanatory course-level)  
+  - “synthèse” (synthesis-based)  
+• **qid** (int): A unique question identifier.  
+• **documents_title** (string): Title(s) or metadata for the document(s).  
+• **output** (string): The annotated answer in French.
+
+----------------------------------------------------------------------------------------------------
+
+## Versions Summary
+
+• **train_v1**: Original stage of the dataset with over 10k QA pairs.  
+• **train_v2**: A refined set of ~7k QA pairs produced after a thorough human review and correction phase (e.g., addressing syntax, relevance, completeness).  
+• **eval**, **test**: Held-out sets of 512 QA items each, created from the corrected dataset (v2).  
+• **test_top1**: Mirrors “test,” but includes automatically retrieved passages (via RAG) as opposed to the original documents used during annotation.
+
+----------------------------------------------------------------------------------------------------
+
+## Source Data and Construction
+
+CQuAE is composed of short extracts from textbooks (e.g., “lelivrescolaire.fr”) and filtered Wikipedia articles chosen to match middle- and high-school curricula in fields like:
+
+• History  
+• Geography  
+• Sciences de la Vie et de la Terre (Biology/Earth Sciences)  
+• Éducation Civique
+
+Wikipedia articles were split into smaller parts (up to three paragraphs) for manageability.
+In total, thousands of texts were collected, though not all were annotated. Two groups of annotators contributed:
+
+• **Group A**: ~20 annotators (non-teachers).  
+• **Group B**: 6 annotators with teaching experience.
+
+Each annotator was asked to produce:
+
+1. A question grounded in the document.  
+2. The type of the question (factual, definition, course, synthesis).  
+3. The document snippet justifying the question.  
+4. Evidence for the answer (the relevant phrases in the text).  
+5. A written answer in French.
+
+----------------------------------------------------------------------------------------------------
+
+## Annotation Process and Types of Questions
+
+Questions were created to vary in difficulty:
+
+1. **Factuelle (Factual)**: Straightforward facts (e.g., event, date, person, location).  
+2. **Définition (Definition)**: Explaining a term or concept.  
+3. **Cours (Course-level)**: More detailed or explanatory answers derived from the text.  
+4. **Synthèse (Synthesis)**: Answers that require reasoned aggregation or interpretation of multiple text elements.
+
+A manual correction phase was then carried out to improve the quality of the initial annotations.
+Approximately 8,000–10,000 items were rechecked to address issues like syntax, missing context, or irrelevance.
+As a result, train_v2 is slightly smaller but generally of higher quality.
+
+----------------------------------------------------------------------------------------------------
+
+## Applications and Examples
+
+CQuAE can be employed for:
+
+• **Training QA Systems**: Evaluate model performance on fact-based vs. complex (explanatory, synthesis) queries.  
+• **Retrieval-Augmented Generation (RAG)**: test_top1 split specifically tests how well a system can retrieve relevant passages from a large corpus.  
+• **Multilingual or Cross-lingual Adaptation**: Although the dataset is in French, it can serve as a testbed for domain adaptation in educational contexts.  
+• **Automatic Question and Answer Generation**: Evaluate how models produce realistic and pedagogically viable Q&A pairs.
+
+----------------------------------------------------------------------------------------------------
+
+## Citation
+
+[CQuAE : Un nouveau corpus de question-réponse pour l’enseignement](https://aclanthology.org/2024.jeptalnrecital-taln.4/) (Gerald et al., JEP/TALN/RECITAL 2024)
+
+If you use or reference CQuAE, please cite:
+@inproceedings{gerald-etal-2024-cquae,
+    title = "{CQ}u{AE} : Un nouveau corpus de question-r{\'e}ponse pour l`enseignement",
+    author = "Gerald, Thomas  and
+      Tamames, Louis  and
+      Ettayeb, Sofiane  and
+      Paroubek, Patrick  and
+      Vilnat, Anne",
+    year = "2024",
+    publisher = "ATALA and AFPC",
+    url = "https://aclanthology.org/2024.jeptalnrecital-taln.4/",
+    language = "fra",
+}