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arXiv 2207.00758cs.CL

MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages

University of Washington(华盛顿大学);Massachusetts Institute of Technology(麻省理工学院);Penn State University(宾夕法尼亚州立大学);University of Wisconsin-Madison(威斯康星大学麦迪逊分校);Studio Ousia(Ousia工作室);RIKEN(日本理化学研究所);Google Research(谷歌研究院);The University of Texas at Austin(德克萨斯大学奥斯汀分校)

Akari Asai, Shayne Longpre, Jungo Kasai, Chia-Hsuan Lee, Rui Zhang, Junjie Hu, Ikuya Yamada, Jonathan H. Clark, Eunsol Choi

英文摘要:

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages. In this task, we adapted two large-scale cross-lingual open-retrieval QA datasets in 14 typologically diverse languages, and newly annotated open-retrieval QA data in 2 underrepresented languages: Tagalog and Tamil. Four teams submitted their systems. The best system leveraging iteratively mined diverse negative examples and larger pretrained models achieves 32.2 F1, outperforming our baseline by 4.5 points. The second best system uses entity-aware contextualized representations for document retrieval, and achieves significant improvements in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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