KNOWLEDGE ENHANCED LATENT RELEVANCE MINING FOR QUESTION ANSWERING

Answer selection which aims to select the most appropriate answer from a pre-selected candidate pool has become increasingly important in a variety of practical applications. Previous work tends to use complex attention mechanisms to capture contextual relevance between question-answer (QA) pairs while ignoring large scale commonsense knowledge. However, this commonsense knowledge provides real-world background information beyond the context, which can help to discover the latent relevance between two sentences. In this paper, we propose to integrate commonsense from the external knowledge graphs (KGs) into the answer selector through a knowledge-aware context-based attention mechanism. To explore the interrelations among knowledge and context, we leverage a compare-aggregate framework to capture more interactive features between questions and answers. Our model is evaluated on two widely-used benchmark QA datasets: WikiQA and TrecQA. The experiments show that our proposed model outperforms the state-of-the-art method.