  Answer selection, which is involved in many natural language processing
applications such as dialog systems and question answering (QA), is an
important yet challenging task in practice, since conventional methods
typically suffer from the issues of ignoring diverse real-world background
knowledge. In this paper, we extensively investigate approaches to enhancing
the 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 sentence
representations by considering a tight interaction with the external knowledge
from KG and the textual information. Then, we develop two kinds of
knowledge-aware attention mechanism to summarize both the context-based and
knowledge-based interactions between questions and answers. To handle the
diversity and complexity of KG information, we further propose a Contextualized
Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge
representation learning with structure information via a customized Graph
Convolutional Network (GCN) and comprehensively learns context-based and
knowledge-based sentence representation via the multi-view knowledge-aware
attention mechanism. We evaluate our method on four widely-used benchmark QA
datasets, including WikiQA, TREC QA, InsuranceQA and Yahoo QA. Results verify
the benefits of incorporating external knowledge from KG, and show the robust
superiority and extensive applicability of our method.
