In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval. DuReader_retrieval contains more than 90K queries and over 8M unique passages from a commercial search engine. To alleviate the shortcomings of other datasets and ensure the quality of our benchmark, we (1) reduce the false negatives in development and test sets by manually annotating results pooled from multiple retrievers, and (2) remove the training queries that are semantically similar to the development and testing queries. Additionally, we provide two out-of-domain testing sets for cross-domain evaluation, as well as a set of human translated queries for for cross-lingual retrieval evaluation. The experiments demonstrate that DuReader_retrieval is challenging and a number of problems remain unsolved, such as the salient phrase mismatch and the syntactic mismatch between queries and paragraphs. These experiments also show that dense retrievers do not generalize well across domains, and cross-lingual retrieval is essentially challenging. DuReader_retrieval is publicly available at https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval.
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
Research Center for SCIR, Harbin Institute of Technology（哈尔滨工业大学SCIR研究中心）；State Key Laboratory of Cognitive Intelligence, iFLYTEK Research, China（科大讯飞研究中心认知智能国家重点实验室）；iFLYTEK AI Research (Hebei), Langfang, China（科大讯飞（河北）人工智能研究院，廊坊）
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；IDEA（深圳国际数字经济研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Cross-Lingual Training with Dense Retrieval for Document Retrieval
David R. Cheriton School of Computer Science, University of Waterloo（滑铁卢大学戴维·R·切里顿计算机科学学院）；Department of Computer Science and Engineering, Penn State University（宾夕法尼亚州立大学计算机科学与工程系）
Vision-Braille: A Curriculum Learning Toolkit and Braille-Chinese Corpus for Braille Translation
State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University（多媒体信息处理国家重点实验室，计算机学院，PKU-Anker LLM实验室，北京大学）；College of Agriculture and Life Sciences, Cornell University（农业与生命科学学院，康奈尔大学）；Computer Science Department, University of California at Los Angeles（计算机科学系，加州大学洛杉矶分校）
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Nanyang Technological University（南洋理工大学）
MITRA: A Large-Scale Parallel Corpus and Multilingual Pretrained Language Model for Machine Translation and Semantic Retrieval for Pāli, Sanskrit, Buddhist Chinese, and Tibetan
Center for Integrated Japanese Studies, Tohoku University（东京东京大学综合日语研究机构）；University of California, Berkeley（加州大学伯克利分校）；Berkeley Artificial Intelligence Research (BAIR)（伯克利人工智能研究（BAIR））
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
Institute of Artificial Intelligence, Soochow University（苏州大学人工智能研究院）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；Pharmcube（药智魔方）；Changping Laboratory（昌平实验室）
