Datasets:

ftvalentini
/

clirudit

	---
	dataset_info:
	- config_name: docs
	features:
	- name: docid
	dtype: string
	- name: title
	dtype: string
	- name: subtitle
	dtype: string
	- name: abstract
	dtype: string
	splits:
	- name: fr.test
	num_bytes: 21532392
	num_examples: 16389
	- name: en.test
	num_bytes: 19436528
	num_examples: 16389
	download_size: 22803206
	dataset_size: 40968920
	- config_name: qrels
	features:
	- name: qid
	dtype: int64
	- name: docid
	dtype: string
	- name: rel
	dtype: int64
	splits:
	- name: en_fr.test
	num_bytes: 10393138
	num_examples: 360596
	- name: en_en.test
	num_bytes: 10393138
	num_examples: 360596
	download_size: 5660924
	dataset_size: 20786276
	- config_name: queries
	features:
	- name: qid
	dtype: int64
	- name: query
	dtype: string
	splits:
	- name: en.test
	num_bytes: 20056464
	num_examples: 357710
	download_size: 7709798
	dataset_size: 20056464
	configs:
	- config_name: docs
	data_files:
	- split: fr.test
	path: docs/fr.test-*
	- split: en.test
	path: docs/en.test-*
	- config_name: qrels
	data_files:
	- split: en_fr.test
	path: qrels/en_fr.test-*
	- split: en_en.test
	path: qrels/en_en.test-*
	- config_name: queries
	data_files:
	- split: en.test
	path: queries/en.test-*
	license: cc-by-nc-4.0
	multilinguality:
	- multilingual
	- translation
	task_categories:
	- text-retrieval
	task_ids:
	- document-retrieval
	language:
	- en
	- fr
	pretty_name: CLIRudit
	source_datasets:
	- original
	tags:
	- research papers
	---

	# Dataset Card for CLIRudit

	<!-- Provide a quick summary of the dataset. -->

	CLIRudit is a dataset for academic Cross-lingual information retrieval (CLIR), consisting of English queries and French documents, based on [Érudit](https://www.erudit.org/en/), a non-profit publishing platform based in Quebec, Canada.

	The CLIRudit dataset follows a TREC-style structure with three main components:

	* Queries: Generated from English keywords of research articles by creating all possible three-keyword combinations.
	For example, an article with keywords {A, B, C, D} would generate four queries: "A, B, C", "A, B, D", "A, C, D", and "B, C, D".
	* Relevance judgments (qrels): A document is considered relevant to a query if its English keywords metadata contains all keywords present in the query, reflecting the assumption that authors want their articles to be discoverable through these keywords.
	* Document collection: Each document consists of concatenated French title, subtitle, and abstract, which serves as the retrieval unit.

	The dataset includes only Érudit research articles containing both abstracts and keywords in both French and English.
	The translations between languages were provided by the original authors of the articles.

	As an _empirical upper bound_, we also include the actual English translations of the French titles, subtitles, and abstracts as documents.
	This represents the best possible performance that can be achieved by a retrieval method with perfect translation.


	## Dataset Details

	### Dataset Description

	<!-- Provide a longer summary of what this dataset is. -->

	- Language(s) (NLP): English, French
	- License: CC BY-NC 4.0. The dataset should not be used for any commercial purpose.


	### Dataset Sources

	<!-- Provide the basic links for the dataset. -->

	- Repository: TBA
	- Paper: [CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents](https://arxiv.org/abs/2504.16264)


	## Uses

	The dataset is meant to be used to evaluate cross-lingual IR models.


	## Dataset Structure

	<!-- 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. -->

	### Data Instances

	A typical instance of the `docs` subset looks like:

	```
	{
	'docid': '000200ar',
	'title': 'Le charme féminin chez les Peuls Djeneri du Mali',
	'subtitle': 'Un « objet » de la nature ou de la culture?',
	'abstract': 'La notion de charme soulève une confusion de sens, car elle est souvent utilisée dans un sens commun...',
	}
	```

	A typical instance of the `queries` subset looks like:

	```
	{
	'qid': 0,
	'query': '"biblioclasts", books with holes, picture books'
	}
	```

	A typical instance of the `qrels` subset looks like:

	```
	{
	'qid': 0,
	'docid': '1089655ar',
	'rel': 1
	}
	```




	### Data Fields

	- `qid`: query id
	- `query`: query text
	- `docid`: document id in Érudit
	- `title`: article title, if any
	- `subtitle`: article subtitle, if any
	- `abstract`: article abstract
	- `rel`: relevance label (currently only positives are labelled with 1)

	<!-- Note that the descriptions can be initialized with the Show Markdown Data Fields output of the [Datasets Tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), you will then only need to refine the generated descriptions. -->



	### Data Splits

	There is one `subset` per dataset component (documents, queries, qrels).
	`split` is used to represent language and train/test split.

	For qrels, we first indicate query lang and then doc lang in the `split` name; e.g., `en_fr.test` means test split judgments with English queries and French documents.




	## Citation

	<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

	BibTeX:

	```bibtex
	@inproceedings{valentini2025clirudit,
	title = "{CLIR}udit: Cross-Lingual Information Retrieval of Scientific Documents",
	author = "Valentini, Francisco and
	Kozlowski, Diego and
	Lariviere, Vincent",
	editor = "Adelani, David Ifeoluwa and
	Arnett, Catherine and
	Ataman, Duygu and
	Chang, Tyler A. and
	Gonen, Hila and
	Raja, Rahul and
	Schmidt, Fabian and
	Stap, David and
	Wang, Jiayi",
	booktitle = "Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)",
	month = nov,
	year = "2025",
	address = "Suzhou, China",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2025.mrl-main.16/",
	doi = "10.18653/v1/2025.mrl-main.16",
	pages = "226--242",
	ISBN = "979-8-89176-345-6",
	}
	```

	APA:

	Francisco Valentini, Diego Kozlowski, and Vincent Lariviere. 2025. CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents. In _Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)_, pages 226–242, Suzhuo, China. Association for Computational Linguistics.