研究神经方法对跨语言信息检索的影响，涵盖多语种新闻与学术文档。
Overview of the TREC 2024 NeuCLIR Track
- 构建中、波斯、俄语新闻及中文学术摘要数据集用于评测
- 共8项任务，274组结果来自5支团队及基线系统
- 覆盖跨语言检索、多语言检索和报告生成等场景
TREC 2024 NeuCLIR 跟踪任务旨在研究神经方法在跨语言信息获取中的作用。该任务创建了包含中文、波斯语和俄语新闻故事以及中文学术摘要的测试集合。NeuCLIR 包含四种任务类型：新闻领域的跨语言信息检索（CLIR）、新闻领域的多语言信息检索（MLIR）、新闻报告生成以及技术文档的跨语言信息检索。共有五个参与团队提交了274组运行结果，涵盖上述四个任务类型的八个具体任务。本文呈现了各任务的描述及可用结果。
原文摘要 · Abstract (English)
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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