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Overview of the TREC 2023 NeuCLIR Track
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The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the impact of neural approaches to cross-language information retrieval. The track has created four collections, large collections of Chinese, Persian, and Russian newswire and a smaller collection of Chinese scientific abstracts. The principal tasks are ranked retrieval of news in one of the three languages, using English topics. Results for a multilingual task, also with English topics but with documents from all three newswire collections, are also reported. New in this second year of the track is a pilot technical documents CLIR task for ranked retrieval of Chinese technical documents using English topics. A total of 220 runs across all tasks were submitted by six participating teams and, as baselines, by track coordinators. Task descriptions and results are presented.
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Cited by 1 Pith paper
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Lost in Transliteration: Bridging the Script Gap in Neural IR
Fine-tuning multilingual retrieval models on an even mix of native and Latin-transliterated training queries largely closes the retrieval gap caused by transliterated queries, with full closure only when scripts overlap.
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