CLEF

The Conference and Labs of the Evaluation Forum (CLEF) is a prominent European-based initiative dedicated to promoting research, innovation, and development in information access systems. A distinguishing feature of CLEF is its strong emphasis on multilinguality and multimodality, addressing the complexities of accessing and processing information across different languages and data types (e.g., text, image, video). CLEF also places significant importance on the advancement of evaluation methodologies, seeking to refine and extend traditional evaluation paradigms like the Cranfield model and explore innovative uses of experimental data.

CLEF's origins trace back to a track within the Text REtrieval Conference (TREC) focused on cross-language information retrieval (IR) for European languages. It became an independent initiative to expand coverage to more languages and a broader array of IR issues. This evolution reflects a broadening understanding of "information access," moving beyond traditional text retrieval to encompass diverse data types and user needs, as evidenced by its inclusion of tasks like species identification from media or health information access , and the integration of the INEX workshop on structured text retrieval.

CLEF's research agenda covers a wide spectrum of information retrieval challenges. While initially focused on monolingual, bilingual, and multilingual text retrieval, its scope has expanded considerably. The initiative now supports investigations into areas such as:

  • Information retrieval for various European and non-European languages.
  • Multimodal information access, integrating data from different sources like text, images, and audio.
  • Specific application domains such as cultural heritage, digital libraries, social media, legal documents, and biomedical information.
  • Evaluation of interactive and conversational information retrieval systems.
  • Analysis of IR test collections and evaluation measures, including reproducibility and replicability issues.

Labs

The core operational structure of CLEF revolves around its Labs. These are essentially evaluation campaigns or tracks where specific research challenges are proposed, and participating research groups from academia and industry develop and test systems to address them. Each lab typically focuses on a particular theme or set of tasks. For instance, CLEF 2024 hosted a variety of labs, including :

  • BioASQ: Large-scale biomedical semantic indexing and question answering.
  • CheckThat!: Tasks related to fact-checking, such as check-worthiness estimation, subjectivity detection, and persuasion technique identification.
  • ImageCLEF: A multimodal challenge involving image annotation, retrieval, and analysis across various domains (e.g., medical, social media).
  • LifeCLEF: Species identification and prediction using various data types (e.g., images, audio), often with a conservation focus.
  • PAN: Lab on stylometry, authorship analysis, and digital text forensics.
  • Touché: Focus on argumentation systems, including argument retrieval and generation.

These labs provide the necessary infrastructure for system testing, tuning, and evaluation. A key contribution of many labs is the creation of reusable test collections (datasets and ground truth) that benefit the wider research community. Lab organizers define the tasks, provide the data, and specify the evaluation protocols. Participants then submit experimental "runs" (system outputs) and often follow up with "Working Notes" that detail their methodologies and findings. This lab-centric structure fosters the development of highly specialized research communities around specific IR challenges. The sustained focus on "evaluation methodologies" , including experiments with novel review processes like result-less review (where papers are initially assessed on methodology and research questions before results are presented) , indicates that CLEF actively contributes to shaping how research is conducted and assessed within these specialized domains, representing a meta-level contribution to the research landscape.

Working Notes

A significant output of participation in CLEF labs is the "Working Notes." These are technical reports authored by participating teams, describing the systems they developed and the experiments they conducted for the lab tasks. Key characteristics include:

  • Publication: CLEF Working Notes are published as part of the CEUR Workshop Proceedings (CEUR-WS.org), making them citable and accessible to the research community. A list of past volumes is available on the CLEF Initiative website.
  • Content: According to the guidelines , working notes should typically cover the tasks performed, main experimental objectives, the approach(es) used (including progress beyond the state-of-the-art), resources employed (datasets, tools), results obtained, a thorough analysis of these results, and perspectives for future work.
  • Format and Submission: There is generally no strict upper page limit, though conciseness and effectiveness are encouraged. Submission is handled electronically, often via EasyChair, and specific formatting templates (e.g., CEUR-WS templates) are provided.
  • Purpose: Working notes serve as a means for rapid dissemination of detailed experimental findings, methodologies, and even negative results, which are valuable for the scientific community. They represent a less formal but often more detailed account of experimental work compared to traditional conference papers. Some labs, even those run on external platforms like Kaggle but affiliated with CLEF (e.g., BirdCLEF+ ), encourage participants to submit working notes to the main CLEF conference, sometimes with awards for the best contributions. This system acts as a bridge, facilitating the quick sharing of experimental insights while still allowing for more polished, archival publications later.

Analyzing a Lab Task

When approaching a CLEF lab task, the analysis should focus on:

  1. Specific Research Questions: Identify the precise questions the lab and its constituent tasks aim to answer.
  2. Data Characteristics: Understand the nature of the provided datasets, paying close attention to multilingual aspects, multimodal features, and any specific annotations.
  3. Evaluation Methodology: Scrutinize the evaluation metrics and protocols, as these are often carefully designed by experts to assess particular system capabilities or nuances of the problem.

An illustrative example is the CLEF 2024 CheckThat! Lab, Task 1: Check-worthiness estimation.

  • Main Problem: The core objective is to determine if a given piece of text—sourced from diverse genres like tweets or political debates—is "check-worthy." This involves assessing whether the text contains a verifiable factual claim and evaluating its potential for causing harm if the claim is false, thereby prioritizing it for fact-checking. For the 2024 edition, this task was offered in Arabic, Dutch, and English.
  • Evaluation Metric: Performance in this task is measured using the macro-averaged F1-score. This metric calculates the F1-score (harmonic mean of precision and recall) for each class (check-worthy and not check-worthy) independently and then averages these scores. This approach ensures that performance on potentially less frequent but critical check-worthy claims is given due weight.