Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets. Under the assumptions that the structured KB is easier to query and the acquired knowledge can help the understanding of unstructured text, our model first accumulates knowledge of entities from a question-related KB subgraph; then reformulates the question in the latent space and reads the texts with the accumulated entity
more » ... nowledge at hand. The evidence from KB and texts are finally aggregated to predict answers. On the widely-used KBQA benchmark WebQSP, our model achieves consistent improvements across settings with different extents of KB incompleteness. 1
doi:10.18653/v1/p19-1417 dblp:conf/acl/XiongYCGW19 fatcat:zqr3f73kfbftvpmjeu4b2alvsi