--------------------------------------------------- TREC 2023 NeuCLIR ---------------------------------------------------
Cross-language Information Retrieval (CLIR) has been studied at TREC and subsequent evaluations for more than twenty years. Prior to the application of deep learning, strong statistical approaches were developed that work well across many languages. As with most other language technologies though, neural computing has led to significant performance improvements in information retrieval. CLIR has just begun to incorporate neural advances. The TREC 2023 NeuCLIR track presents a cross-language information retrieval challenge. NeuCLIR topics are written in English. NeuCLIR has three target language collections in Chinese, Persian, and Russian. Topics are written in the traditional TREC format: a short title and a sentence-length description. Systems are to return a ranked list of documents for each topic. Results will be pooled, and systems will be evaluated on a range of metrics. This year, we include two new challenges: retrieval from a corpus that includes multiple languages, and retrieval from a corpus of technical documents. --- Task Description --- * Single-Language News Retrieval * Multi-Language News Retrieval * Single-Language Technical Abstract Retrieval * Website: https://neuclir.github.io/ * Mailing List: https://groups.google.com/g/neuclir-participants --- Important Dates --- Already: Evaluation document collection released Already: Track guidelines released Already: CLIR/MLIR: Topics released June 30, 2023: CLIR/MLIR: Submissions due to NIST June 30, 2023: Technical Document Topic Release August 1, 2023: Technical Document Submission September 30, 2023: Results distributed to participants November 2023: TREC 2023 --- Organizing Committee --- Dawn Lawrie, Johns Hopkins University, HLTCOE Sean MacAvaney, University of Glasgow James Mayfield, Johns Hopkins University, HLTCOE Paul McNamee, Johns Hopkins University, HLTCOE Douglas W. Oard, University of Maryland Luca Soldaini, Allen Institute for AI Eugene Yang, Johns Hopkins University, HLTCOE
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