A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering
Research output: Contribution to journal › Journal article › Research › peer-review
Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design of effective algorithms for relation detection. Conventional methods model questions and candidate relations separately through the knowledge bases (KBs) without considering the rich word-level interactions between them. This approach may result in local optimal results. This article presents a global–local attentive relation detection model (GLAR) that utilizes the local module to learn the features of word-level interactions and employs the global module to acquire nonlinear relationships between questions and their candidate relations located in KBs. This article also reports on the application of an end-to-end retrieval-based KBQA system incorporating the proposed relation detection model. Experimental results obtained on two datasets demonstrated GLAR's remarkable performance in the relation detection tas...
Original language | English |
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Journal | IEEE Transactions on Artificial Intelligence |
Volume | 2 |
Issue number | 2 |
Pages (from-to) | 200-212 |
DOIs | |
Publication status | Published - 2021 |
ID: 300671974