Knowledge Base Relation Detection via Multi-View Matching
This work addresses relation detection for improving KBQA systems, representing an incremental advancement with specific performance gains.
The paper tackled the problem of relation detection in Knowledge Base Question Answering by proposing a multi-view matching model that leverages more information from questions and the knowledge base, achieving state-of-the-art results on SimpleQuestions and WebQSP datasets.
Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a KB relation detection model via multi-view matching which utilizes more useful information extracted from question and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are designed in an end-to-end trainable neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results.