End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on datadriven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptomdisease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats stateof-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.

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

[2]  Joelle Pineau,et al.  The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems , 2015, SIGDIAL Conference.

[3]  Gökhan Tür,et al.  Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM , 2016, INTERSPEECH.

[4]  Zhaochun Ren,et al.  Hierarchical Variational Memory Network for Dialogue Generation , 2018, WWW.

[5]  David Vandyke,et al.  A Network-based End-to-End Trainable Task-oriented Dialogue System , 2016, EACL.

[6]  Steve J. Young,et al.  The Hidden Agenda User Simulation Model , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Tsung-Hsien Wen,et al.  Neural Belief Tracker: Data-Driven Dialogue State Tracking , 2016, ACL.

[8]  Xin Wang,et al.  Video Captioning via Hierarchical Reinforcement Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Jianfeng Gao,et al.  BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems , 2016, AAAI.

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

[11]  Anton Leuski,et al.  ScoutBot: A Dialogue System for Collaborative Navigation , 2018, ACL.

[12]  Jianfeng Gao,et al.  End-to-End Task-Completion Neural Dialogue Systems , 2017, IJCNLP.

[13]  Kai-Fu Tang,et al.  Inquire and Diagnose : Neural Symptom Checking Ensemble using Deep Reinforcement Learning , 2016 .

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

[15]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[16]  Jason Weston,et al.  Learning End-to-End Goal-Oriented Dialog , 2016, ICLR.

[17]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[18]  Min-Yen Kan,et al.  Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures , 2018, ACL.

[19]  Christopher D. Manning,et al.  Key-Value Retrieval Networks for Task-Oriented Dialogue , 2017, SIGDIAL Conference.

[20]  Pascale Fung,et al.  Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems , 2018, ACL.

[21]  Zhoujun Li,et al.  Building Task-Oriented Dialogue Systems for Online Shopping , 2017, AAAI.

[22]  Christopher D. Manning,et al.  A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue , 2017, EACL.

[23]  Quoc V. Le,et al.  A Neural Conversational Model , 2015, ArXiv.

[24]  Joelle Pineau,et al.  How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation , 2016, EMNLP.

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

[26]  Maxine Eskénazi,et al.  Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability , 2017, SIGDIAL Conference.

[27]  Xuanjing Huang,et al.  Task-oriented Dialogue System for Automatic Diagnosis , 2018, ACL.