Related papers: Overview of the TREC 2024 NeuCLIR Track

Overview of the TREC 2024 NeuCLIR Track

URL: http://arxiv.org/abs/2509.14355v1
Date: Wed, 17 Sep 2025 18:36:38 GMT
Title: Overview of the TREC 2024 NeuCLIR Track
Authors: Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas W. Oard, Luca Soldaini, Eugene Yang,
Abstract summary: The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the effect of neural approaches on cross-language information access.<n>NeuCLIR includes four task types: Cross-Language Information Retrieval (CLIR) from news, Multilingual Information Retrieval (MLIR) from news, Report Generation from news, and CLIR from technical documents.
Score: 43.84164712459855
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the effect of neural approaches on cross-language information access. The track has created test collections containing Chinese, Persian, and Russian news stories and Chinese academic abstracts. NeuCLIR includes four task types: Cross-Language Information Retrieval (CLIR) from news, Multilingual Information Retrieval (MLIR) from news, Report Generation from news, and CLIR from technical documents. A total of 274 runs were submitted by five participating teams (and as baselines by the track coordinators) for eight tasks across these four task types. Task descriptions and the available results are presented.

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