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	Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs
	Ying Shen; Yang Deng; Min Yang; Yaliang Li; Nan Du; Wei Fan; Kai Lei
	2018
会议日期	2018
会议地点	美国芝加哥
英文摘要	Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop a context-knowledge interactive learning architecture, in which a context-guided attentive convolutional neural network (CNN) is designed to integrate knowledge embeddings into sentence representations. Besides, a knowledge-aware attention mechanism is presented to attend interrelations between each segments of QA pairs. KABLSTM is evaluated on two widely-used benchmark QA datasets: WikiQA and TREC QA. Experiment results demonstrate that KABLSTM has robust superiority over competitors and sets state-of-the-art.
语种	英语
URL标识	查看原文
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14090]
专题	深圳先进技术研究院_数字所
推荐引用方式 GB/T 7714	Ying Shen,Yang Deng,Min Yang,et al. Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs[C]. 见:. 美国芝加哥. 2018.

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