Paper ID: 2111.06086

A Chinese Multi-type Complex Questions Answering Dataset over Wikidata

Jianyun Zou, Min Yang, Lichao Zhang, Yechen Xu, Qifan Pan, Fengqing Jiang, Ran Qin, Shushu Wang, Yifan He, Songfang Huang, Zhou Zhao

Complex Knowledge Base Question Answering is a popular area of research in the past decade. Recent public datasets have led to encouraging results in this field, but are mostly limited to English and only involve a small number of question types and relations, hindering research in more realistic settings and in languages other than English. In addition, few state-of-the-art KBQA models are trained on Wikidata, one of the most popular real-world knowledge bases. We propose CLC-QuAD, the first large scale complex Chinese semantic parsing dataset over Wikidata to address these challenges. Together with the dataset, we present a text-to-SPARQL baseline model, which can effectively answer multi-type complex questions, such as factual questions, dual intent questions, boolean questions, and counting questions, with Wikidata as the background knowledge. We finally analyze the performance of SOTA KBQA models on this dataset and identify the challenges facing Chinese KBQA.

Submitted: Nov 11, 2021