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Computer Science > Information Retrieval

arXiv:2304.12367 (cs)
[Submitted on 24 Apr 2023 (v1), last revised 24 Sep 2023 (this version, v2)]

Title:Overview of the TREC 2022 NeuCLIR Track

Authors:Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas W. Oard, Luca Soldaini, Eugene Yang
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Abstract:This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this year's track was ad hoc ranked retrieval of Chinese, Persian, or Russian newswire documents using queries expressed in English. Topics were developed using standard TREC processes, except that topics developed by an annotator for one language were assessed by a different annotator when evaluating that topic on a different language. There were 172 total runs submitted by twelve teams.
Comments: 22 pages, 13 figures, 10 tables. Part of the Thirty-First Text REtrieval Conference (TREC 2022) Proceedings. Replace the misplaced Russian result table
Subjects: Information Retrieval (cs.IR)
Cite as: arXiv:2304.12367 [cs.IR]
  (or arXiv:2304.12367v2 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2304.12367

Submission history

From: Eugene Yang [view email]
[v1] Mon, 24 Apr 2023 18:04:38 UTC (735 KB)
[v2] Sun, 24 Sep 2023 04:55:27 UTC (736 KB)
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