Measuring advances in retrieval requires test collections with relevance judgments that can faithfully distinguish systems. This paper presents NeuCLIRTech, an evaluation collection for cross-language retrieval over technical information. The collection consists of technical documents written natively in Chinese and those same documents machine translated into English. It includes 110 queries with relevance judgments. The collection supports two retrieval scenarios: monolingual retrieval in Chinese, and cross-language retrieval with English as the query language. NeuCLIRTech combines the TREC NeuCLIR track topics of 2023 and 2024. The 110 queries with 35,962 document judgments provide strong statistical discriminatory power when trying to distinguish retrieval approaches. A fusion baseline of strong neural retrieval systems is included so that developers of reranking algorithms are not reliant on BM25 as their first stage retriever. The dataset and artifacts are released on Huggingface Datasets

翻译：衡量检索技术的进展需要能够忠实区分系统性能的、具备相关性标注的测试集。本文提出NeuCLIRTech，一个面向技术信息跨语言检索的评估数据集。该数据集包含原生中文撰写的技术文档及其机器翻译的英文版本，收录了110个带相关性标注的查询。本数据集支持两种检索场景：中文单语检索，以及以英文为查询语言的跨语言检索。NeuCLIRTech整合了TREC NeuCLIR赛道2023年与2024年的主题。包含35,962份文档标注的110个查询在区分不同检索方法时具备较强的统计判别力。我们提供了基于强神经检索系统的融合基线，使得重排序算法的开发者无需依赖BM25作为其第一阶段的检索器。数据集及相关资源已在Huggingface Datasets平台发布。