Question-Aware Memory Network for Multi-hop Question Answering in Human-Robot Interaction

Abstract

Knowledge graph question answering is an important technology in intelligent human-robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.

arXiv comment25 pages, 4 figures

References in corpus (14)

• Sequence to Sequence Learning with Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• End-To-End Memory Networks
• Large-scale Simple Question Answering with Memory Networks
• Advances and Challenges in Conversational Recommender Systems: A Survey
• Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
• Variational Reasoning for Question Answering with Knowledge Graph
• Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
• Lambda Dependency-Based Compositional Semantics
• An Interpretable Reasoning Network for Multi-Relation Question Answering
• Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
• Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs
• Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
• Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction