Knowledge Aware Conversation Generation with Reasoning on Augmented Graph

Two types of knowledge, factoid knowledge from graphs and non-factoid knowledge from unstructured documents, have been studied for knowledge aware open-domain conversation generation, in which edge information in graphs can help generalization of knowledge selectors, and text sentences can provide rich information for response generation. Fusion of knowledge triples and sentences might yield mutually reinforcing advantages for conversation generation, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, augmented knowledge graph containing both factoid and non-factoid knowledge, knowledge selector, and response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning that is more flexible in comparison with previous one-hop knowledge selection models. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate that supported by such unified knowledge and knowledge selection method, our system can generate more appropriate and informative responses than baselines.

[1]  Alexander J. Smola,et al.  Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning , 2017, ICLR.

[2]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[3]  Mitesh M. Khapra,et al.  Towards Exploiting Background Knowledge for Building Conversation Systems , 2018, EMNLP.

[4]  Jayant Krishnamurthy,et al.  Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge , 2017, AAAI.

[5]  Christopher D. Manning,et al.  Get To The Point: Summarization with Pointer-Generator Networks , 2017, ACL.

[6]  R. J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[7]  Hang Li,et al.  Neural Responding Machine for Short-Text Conversation , 2015, ACL.

[8]  Joelle Pineau,et al.  Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models , 2015, AAAI.

[9]  Chin-Yew Lin,et al.  Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics , 2004, ACL.

[10]  Wenhan Xiong,et al.  DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning , 2017, EMNLP.

[11]  Xiaoyan Zhu,et al.  Commonsense Knowledge Aware Conversation Generation with Graph Attention , 2018, IJCAI.

[12]  Jason Weston,et al.  Wizard of Wikipedia: Knowledge-Powered Conversational agents , 2018, ICLR.

[13]  Xianchao Wu,et al.  Dialog Generation Using Multi-Turn Reasoning Neural Networks , 2018, NAACL.

[14]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[15]  Erik Cambria,et al.  Augmenting End-to-End Dialogue Systems With Commonsense Knowledge , 2018, AAAI.

[16]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[17]  Tom M. Mitchell,et al.  Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.

[18]  Hyunki Kim,et al.  Open domain question answering using Wikipedia-based knowledge model , 2014, Inf. Process. Manag..

[19]  Richard Socher,et al.  Multi-Hop Knowledge Graph Reasoning with Reward Shaping , 2018, EMNLP.

[20]  Milica Gasic,et al.  POMDP-Based Statistical Spoken Dialog Systems: A Review , 2013, Proceedings of the IEEE.

[21]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[22]  Ruslan Salakhutdinov,et al.  Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text , 2018, EMNLP.

[23]  Yang Feng,et al.  Knowledge Diffusion for Neural Dialogue Generation , 2018, ACL.

[24]  Zhen Xu,et al.  Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling , 2016, ArXiv.

[25]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[26]  한상도,et al.  Exploiting knowledge base to generate responses for natural language dialog listening agents. , 2015 .

[27]  Yu Zhang,et al.  Flexible End-to-End Dialogue System for Knowledge Grounded Conversation , 2017, ArXiv.

[28]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[29]  Elena Paslaru Bontas Simperl,et al.  A Neural Network Approach for Knowledge-Driven Response Generation , 2016, COLING.

[30]  Ali Farhadi,et al.  Bidirectional Attention Flow for Machine Comprehension , 2016, ICLR.

[31]  Ming-Wei Chang,et al.  A Knowledge-Grounded Neural Conversation Model , 2017, AAAI.

[32]  Rajarshi Das,et al.  Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks , 2016, EACL.

[33]  Alan Ritter,et al.  Data-Driven Response Generation in Social Media , 2011, EMNLP.

[34]  Rajarshi Das,et al.  Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks , 2017, ACL.