Path Selection for Chinese Knowledge Base Question Answering
WU Kun1, ZHOU Xiabing1, LI Zhenghua1, LIANG Xingwei2, CHEN Wenliang1
1.School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China; 2.Konka Group Co., Ltd, Shenzhen, Guangdong 518000, China
Abstract：Path selection, as a key step in the Knowledge Base Question Answering (KBQA) task, relies on the the semantic similarity between a question and candidate paths. To deal with massive unseen relations in the test set, a method based on dynamic sampling of negative examples is proposed to enrich the relations in the training set. In the prediction phase, two path pruning methods, i.e., the classification method and the beam search method, are compared to tackle the explosion of candidate paths. On the CCKS 2019-CKBQA evaluation data set containing simple and complex problems, the proposed method achieves an average F1 value of 0.694 for the single-model system, and 0.731 for the ensemble system.
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2021, Vol. 35 
