A Survey of Document Grounded Dialogue Systems (DGDS)

Dialogue system (DS) attracts great attention from industry and academia because of its wide application prospects. Researchers usually divide the DS according to the function. However, many conversations require the DS to switch between different functions. For example, movie discussion can change from chit-chat to QA, the conversational recommendation can transform from chit-chat to recommendation, etc. Therefore, classification according to functions may not be enough to help us appreciate the current development trend. We classify the DS based on background knowledge. Specifically, study the latest DS based on the unstructured document(s). We define Document Grounded Dialogue System (DGDS) as the DS that the dialogues are centering on the given document(s). The DGDS can be used in scenarios such as talking over merchandise against product Manual, commenting on news reports, etc. We believe that extracting unstructured document(s) information is the future trend of the DS because a great amount of human knowledge lies in these document(s). The research of the DGDS not only possesses a broad application prospect but also facilitates AI to better understand human knowledge and natural language. We analyze the classification, architecture, datasets, models, and future development trends of the DGDS, hoping to help researchers in this field.

[1]  Mitesh M. Khapra,et al.  On Knowledge distillation from complex networks for response prediction , 2019, NAACL.

[2]  Bowen Zhou,et al.  Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs , 2019, ACL.

[3]  Weiming Zhang,et al.  Neural Machine Reading Comprehension: Methods and Trends , 2019, Applied Sciences.

[4]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[5]  Claire Cardie,et al.  DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension , 2019, TACL.

[6]  Xiang Zhang,et al.  Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems , 2015, ICLR.

[7]  Zhe Gan,et al.  Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization , 2018, NeurIPS.

[8]  Cristian Danescu-Niculescu-Mizil,et al.  Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives , 2019, NAACL.

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

[10]  Hao Wang,et al.  c-TextGen: Conditional Text Generation for Harmonious Human-Machine Interaction , 2019, ArXiv.

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

[12]  Mohammed J. Zaki,et al.  GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension , 2019, IJCAI.

[13]  Guillaume Bouchard,et al.  Interpretation of Natural Language Rules in Conversational Machine Reading , 2018, EMNLP.

[14]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[15]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[16]  Yinong Long,et al.  A Knowledge Enhanced Generative Conversational Service Agent , 2017 .

[17]  Xiangru Tang,et al.  Knowledge-Aware Self-Attention Networks for Document Grounded Dialogue Generation , 2019, KSEM.

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

[19]  Xiaodong Liu,et al.  Stochastic Answer Networks for Machine Reading Comprehension , 2017, ACL.

[20]  Mitesh M. Khapra,et al.  Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses , 2019, AAAI.

[21]  Nicola De Cao,et al.  Question Answering by Reasoning Across Documents with Graph Convolutional Networks , 2018, NAACL.

[22]  Mitesh M. Khapra,et al.  Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph , 2018, AAAI.

[23]  M. de Rijke,et al.  Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation , 2019, AAAI.

[24]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

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

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

[27]  Alec Radford,et al.  Improving Language Understanding by Generative Pre-Training , 2018 .

[28]  Alon Lavie,et al.  METEOR: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments , 2007, WMT@ACL.

[29]  Peter Clark,et al.  Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering , 2018, EMNLP.

[30]  Hang Li,et al.  “ Tony ” DNN Embedding for “ Tony ” Selective Read for “ Tony ” ( a ) Attention-based Encoder-Decoder ( RNNSearch ) ( c ) State Update s 4 SourceVocabulary Softmax Prob , 2016 .

[31]  Gerhard Weikum,et al.  Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion , 2019, CIKM.

[32]  Jianfeng Gao,et al.  A Human Generated MAchine Reading COmprehension Dataset , 2018 .

[33]  Xiaodong Liu,et al.  Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading , 2019, ACL.

[34]  Dongyan Zhao,et al.  RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems , 2017, AAAI.

[35]  Luke Zettlemoyer,et al.  E3: Entailment-driven Extracting and Editing for Conversational Machine Reading , 2019, ACL.

[36]  W. Bruce Croft,et al.  BERT with History Answer Embedding for Conversational Question Answering , 2019, SIGIR.

[37]  Hai Zhao,et al.  Modeling Multi-turn Conversation with Deep Utterance Aggregation , 2018, COLING.

[38]  Eunsol Choi,et al.  QuAC: Question Answering in Context , 2018, EMNLP.

[39]  Gabriel Stanovsky,et al.  DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs , 2019, NAACL.

[40]  Rui Yan,et al.  "Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI , 2018, IJCAI.

[41]  Junji Tomita,et al.  A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension , 2019, Proceedings of the First Workshop on NLP for Conversational AI.

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

[43]  Dan Roth,et al.  Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences , 2018, NAACL.

[44]  Phil Blunsom,et al.  Teaching Machines to Read and Comprehend , 2015, NIPS.

[45]  Zheng-Yu Niu,et al.  Knowledge Aware Conversation Generation with Reasoning on Augmented Graph , 2019, ArXiv.

[46]  Harry Shum,et al.  The Design and Implementation of XiaoIce, an Empathetic Social Chatbot , 2018, CL.

[47]  Jonathan Berant,et al.  Evaluating Semantic Parsing against a Simple Web-based Question Answering Model , 2017, *SEMEVAL.

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

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

[50]  Arantxa Otegi,et al.  Survey on evaluation methods for dialogue systems , 2019, Artificial Intelligence Review.

[51]  W. Bruce Croft,et al.  Attentive History Selection for Conversational Question Answering , 2019, CIKM.

[52]  Jiliang Tang,et al.  A Survey on Dialogue Systems: Recent Advances and New Frontiers , 2017, SKDD.

[53]  Mohit Iyyer,et al.  Generating Question-Answer Hierarchies , 2019, ACL.

[54]  Yoshua Bengio,et al.  HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering , 2018, EMNLP.

[55]  Yelong Shen,et al.  ReasoNet: Learning to Stop Reading in Machine Comprehension , 2016, CoCo@NIPS.

[56]  Jason Weston,et al.  ParlAI: A Dialog Research Software Platform , 2017, EMNLP.

[57]  Dongyan Zhao,et al.  A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots , 2019, IJCAI.

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

[59]  Harry Shum,et al.  From Eliza to XiaoIce: challenges and opportunities with social chatbots , 2018, Frontiers of Information Technology & Electronic Engineering.

[60]  Nelson F. Liu,et al.  Crowdsourcing Multiple Choice Science Questions , 2017, NUT@EMNLP.

[61]  Filip Radlinski,et al.  Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences , 2019, SIGdial.

[62]  Chenguang Zhu,et al.  SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering , 2018, ArXiv.

[63]  Ahmed Elgohary,et al.  A dataset and baselines for sequential open-domain question answering , 2018, EMNLP.

[64]  Alan W. Black,et al.  A Dataset for Document Grounded Conversations , 2018, EMNLP.

[65]  Xiaodong Liu,et al.  A Hybrid Retrieval-Generation Neural Conversation Model , 2019, CIKM.

[66]  An Yang,et al.  Machine Reading Comprehension: a Literature Review , 2019, ArXiv.

[67]  Nobuhiro Kaji,et al.  Conversation Initiation by Diverse News Contents Introduction , 2019, NAACL-HLT.

[68]  Yue Zhang,et al.  Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks , 2018, ArXiv.

[69]  Jason Weston,et al.  Reading Wikipedia to Answer Open-Domain Questions , 2017, ACL.

[70]  M. de Rijke,et al.  RefNet: A Reference-aware Network for Background Based Conversation , 2019, AAAI.

[71]  Jason Weston,et al.  Retrieve and Refine: Improved Sequence Generation Models For Dialogue , 2018, SCAI@EMNLP.

[72]  Nenghai Yu,et al.  Deliberation Networks: Sequence Generation Beyond One-Pass Decoding , 2017, NIPS.

[73]  Bonnie L. Webber,et al.  Edina: Building an Open Domain Socialbot with Self-dialogues , 2017, ArXiv.

[74]  Ying Chen,et al.  Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network , 2018, ACL.

[75]  Marilyn A. Walker,et al.  Quantitative and Qualitative Evaluation of Darpa Communicator Spoken Dialogue Systems , 2001, ACL.

[76]  Ming-Wei Chang,et al.  Search-based Neural Structured Learning for Sequential Question Answering , 2017, ACL.

[77]  Gu Yingjie,et al.  TT-Net: Topic Transfer-Based Neural Network for Conversational Reading Comprehension , 2019, IEEE Access.

[78]  Ruhi Sarikaya.  An overview of the system architecture and key components The Technology Behind Personal Digital Assistants , 2022 .

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

[80]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[81]  Joelle Pineau,et al.  Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems , 2015 .

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

[83]  Abhishek Sharma,et al.  Neural Conversational QA: Learning to Reason vs Exploiting Patterns , 2019, EMNLP.

[84]  Mathias Niepert,et al.  Attending to Future Tokens for Bidirectional Sequence Generation , 2019, EMNLP.

[85]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[86]  Jason Weston,et al.  Personalizing Dialogue Agents: I have a dog, do you have pets too? , 2018, ACL.

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

[88]  Zheng-Yu Niu,et al.  Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs , 2019, EMNLP.

[89]  Yang Feng,et al.  Incremental Transformer with Deliberation Decoder for Document Grounded Conversations , 2019, ACL.

[90]  M. de Rijke,et al.  Improving Background Based Conversation with Context-aware Knowledge Pre-selection , 2019, ArXiv.

[91]  Jian Zhang,et al.  SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.

[92]  Gregory A. Sanders,et al.  DARPA communicator dialog travel planning systems: the june 2000 data collection , 2001, INTERSPEECH.

[93]  Kaixuan Li,et al.  First-principle study on honeycomb fluorated-InTe monolayer with large Rashba spin splitting and direct bandgap , 2019, Applied Surface Science.

[94]  Tim Paek.  Empirical Methods for Evaluating Dialog Systems , 2001, SIGDIAL Workshop.

[95]  Eunsol Choi,et al.  CONVERSATIONAL MACHINE COMPREHENSION , 2019 .

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

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

[98]  Ming Zhou,et al.  Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base , 2018, NeurIPS.

[99]  Samira Shaikh,et al.  A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions , 2019, ArXiv.

[100]  Nan Jiang,et al.  LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics , 2018, NAACL.

[101]  Philip Bachman,et al.  NewsQA: A Machine Comprehension Dataset , 2016, Rep4NLP@ACL.

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

[103]  Jason Weston,et al.  The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations , 2015, ICLR.

[104]  Lihong Li,et al.  Neural Approaches to Conversational AI , 2019, Found. Trends Inf. Retr..

[105]  Peter Clark.  Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge! , 2015, AAAI.

[106]  Yi Pan,et al.  Conversational AI: The Science Behind the Alexa Prize , 2018, ArXiv.

[107]  Jun Huang,et al.  Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems , 2018, SIGIR.

[108]  Yun-Nung Chen,et al.  An Empirical Study of Content Understanding in Conversational Question Answering , 2020, AAAI.

[109]  Jianfeng Gao,et al.  Challenges in Building Intelligent Open-domain Dialog Systems , 2019, ACM Trans. Inf. Syst..

[110]  Ming-Wei Chang,et al.  Natural Questions: A Benchmark for Question Answering Research , 2019, TACL.

[111]  Philip S. Yu,et al.  Review Conversational Reading Comprehension , 2019, ArXiv.

[112]  Jinho D. Choi,et al.  Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog , 2018, NAACL.

[113]  Jianfeng Gao,et al.  A Diversity-Promoting Objective Function for Neural Conversation Models , 2015, NAACL.

[114]  Jason Weston,et al.  Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.

[115]  Sebastian Riedel,et al.  Constructing Datasets for Multi-hop Reading Comprehension Across Documents , 2017, TACL.

[116]  Joelle Pineau,et al.  A Survey of Available Corpora for Building Data-Driven Dialogue Systems , 2015, Dialogue Discourse.

[117]  Frances S. Grodzinsky,et al.  The anatomy of programming languages , 1993 .

[118]  Joseph Weizenbaum,et al.  ELIZA—a computer program for the study of natural language communication between man and machine , 1966, CACM.

[119]  Dilek Z. Hakkani-Tür,et al.  Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations , 2019, INTERSPEECH.

[120]  Yiming Yang,et al.  XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.

[121]  George R. Doddington,et al.  Automatic Evaluation of Machine Translation Quality Using N-gram Co-Occurrence Statistics , 2002 .

[122]  Zachary Chase Lipton,et al.  Born Again Neural Networks , 2018, ICML.

[123]  Gerhard Weikum,et al.  Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs , 2019, SIGIR.

[124]  Oriol Vinyals,et al.  Adversarial Evaluation of Dialogue Models , 2017, ArXiv.

[125]  Kenneth Mark Colby,et al.  Artificial Paranoia , 1975, Artif. Intell..

[126]  Shijie Chen,et al.  Technical report on Conversational Question Answering , 2019, ArXiv.

[127]  Joelle Pineau,et al.  Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses , 2017, ACL.

[128]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[129]  Jason Weston,et al.  Memory Networks , 2014, ICLR.

[130]  Mark Yatskar,et al.  A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC , 2018, NAACL.

[131]  Danqi Chen,et al.  CoQA: A Conversational Question Answering Challenge , 2018, TACL.

[132]  Nobuhiro Kaji,et al.  Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems , 2017, ACL.

[133]  Jonathan Berant,et al.  The Web as a Knowledge-Base for Answering Complex Questions , 2018, NAACL.

[134]  Xueqi Cheng,et al.  An Adaptive Framework for Conversational Question Answering , 2019, AAAI.

[135]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

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

[137]  Yun-Nung Chen,et al.  FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension , 2019, EMNLP.

[138]  Christopher Clark,et al.  Simple and Effective Multi-Paragraph Reading Comprehension , 2017, ACL.

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

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

[141]  Jungang Xu,et al.  A Survey on Neural Machine Reading Comprehension , 2019, ArXiv.

[142]  Simon Ostermann,et al.  SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge , 2018, *SEMEVAL.