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Answer selection, which is involved in many natural language processingapplications such as dialog systems and question answering (QA), is animportant yet challenging task in practice, since conventional methodstypically suffer from the issues of ignoring diverse real-world backgroundknowledge. In this paper, we extensively investigate approaches to enhancingthe answer selection model with external knowledge from knowledge graph (KG).First, we present a context-knowledge interaction learning framework,Knowledge-aware Neural Network (KNN), which learns the QA sentencerepresentations by considering a tight interaction with the external knowledgefrom KG and the textual information. Then, we develop two kinds ofknowledge-aware attention mechanism to summarize both the context-based andknowledge-based interactions between questions and answers. To handle thediversity and complexity of KG information, we further propose a ContextualizedKnowledge-aware Attentive Neural Network (CKANN), which improves the knowledgerepresentation learning with structure information via a customized GraphConvolutional Network (GCN) and comprehensively learns context-based andknowledge-based sentence representation via the multi-view knowledge-awareattention mechanism. We evaluate our method on four widely-used benchmark QAdatasets, including WikiQA, TREC QA, InsuranceQA and Yahoo QA. Results verifythe benefits of incorporating external knowledge from KG, and show the robustsuperiority and extensive applicability of our method.

Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge