Dataset Card for CLIRudit

CLIRudit is a dataset for academic Cross-lingual information retrieval (CLIR), consisting of English queries and French documents, based on Érudit, 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

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

Dataset Sources

• Repository: https://github.com/ftvalentini/CLIRudit
• Paper: CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents

Uses

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

Dataset Structure

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)

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

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.