Cited by 580Speech and dialogue systemsAI in Service InteractionsTopic Modeling

The DOI matches its Crossref record; 139 of the 139 references it cites were found in a registry or an index.

A Survey on Dialogue Systems: Recent Advances and New Frontiers

Open access

Chen, Hongshen, Liu, Xiaorui, Yin, Dawei, Tang, Jiliang·2017·ACM SIGKDD Explorations Newsletter

Open this paper in CoChat — free account Open PDF

Abstract

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1145/3166054.3166058

Cites

139 of 139 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Term-Weighting Approaches in Automatic Text Retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 11,421
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390

Show all 134 references

Research works printed but not linked: 26

Bahl L., 1986, IEEE International Conference on ICASSP'86, 11, 49
Brown P. F., 1987, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
Henderson M., 2013, Proceedings of the SIGDIAL 2013 Conference, 467
Hu B., 2014, Advances in neural information processing systems, 2042
Kingma D. P., 2014, ICLR
Lee S., 2013, SIGDIAL Conference, 442
Lee S., 2013, SIGDIAL Conference, 414
Lu Z., 2013, International Conference on Neural Information Processing Systems, 1367
Mesnil G., 2013, Interspeech
Möller S., 2006, Ninth International Conference on Spoken Language Processing
Mou L., Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3349--3358, Osaka, Japan, December 2016. The COLING 2016 Organizing Committee.
Papernot N., 2017, ICLR
Ren H., 2013, SIGDIAL Conference, 457
10.5555/2145432.2145500 · DOI
Sohn K., 2015, International Conference on Neural Information Processing Systems, 3483
Tran V. K., 2017, PACLING
Vougiouklis P., Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3370--3380, Osaka, Japan, December 2016. The COLING 2016 Organizing Committee.
Wang Z., 2013, SIGDIAL Conference, 423
Williams J., 2013, SIGDIAL Conference, 433
Williams J., 2013, Proceedings of the SIGDIAL 2013 Conference, 404
10.5555/2390444.2390461 · DOI
Yao K., 2013, Interspeech
10.5555/3060832.3061037 · DOI
10.5555/647621.732232 · DOI
10.5555/1996889.1996934 · DOI
Zhou H., 2016, COLING, 2032

Sorted by citations

Graph Neural Networks for Social Recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., & Yin, D. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313488 · Cited by 2,179
Self-citation
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
Advances and challenges in conversational recommender systems: A survey
Gao, C., Lei, W., He, X., de Rijke, M., & Chua, T. (2021) · AI Open · doi:10.1016/j.aiopen.2021.06.002 · Cited by 390
Survey on evaluation methods for dialogue systems
Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09866-x · Cited by 361
Graph Neural Networks for Natural Language Processing: A Survey
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 417
Recent advances in deep learning based dialogue systems: a systematic survey
Ni, J., Young, T., Pandelea, V., Xue, F., & Cambria, E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10248-8 · Cited by 313
A Survey on Chatbot Implementation in Customer Service Industry through Deep Neural Networks
Nuruzzaman, M., & Hussain, O. K. (2018) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe.2018.00019 · Cited by 266
Medical deep learning—A systematic meta-review
Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106874 · Cited by 289
Users' experiences with chatbots: findings from a questionnaire study
Følstad, A., & Brandtzæg, P. B. (2020) · Quality and User Experience · doi:10.1007/s41233-020-00033-2 · Cited by 324
Future directions for chatbot research: An interdisciplinary research agenda
Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247
Understanding the user experience of customer service chatbots: An experimental study of chatbot interaction design
Haugeland, I. K. F., Følstad, A., Taylor, C., & Bjørkli, C. A. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102788 · Cited by 235
A critical review of state‐of‐the‐art chatbot designs and applications
Luo, B., Lau, R. Y. K., Li, C., & Si, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1434 · Cited by 266
A survey on empathetic dialogue systems
Ma, Y., Nguyen, K. L., Xing, F., & Cambria, E. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.011 · Cited by 230
Dense Text Retrieval Based on Pretrained Language Models: A Survey
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3637870 · Cited by 211
“As one of the most essential techniques to overcome information overload, text retrieval systems have been widely employed to support many downstream applications, including question answering [1], [2], dialog system [3], [4], entity linking [5], [6] and Web search [7].”
Background
Neural Approaches to Conversational AI
Gao, J., Galley, M., & Li, L. (2019) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000074 · Cited by 424
Recent advances and challenges in task-oriented dialog systems
Zhang, Z., Takanobu, R., Zhu, Q., Huang, M., & Zhu, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1692-3 · Cited by 216
Conversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168
A Review of Deep Learning Based Speech Synthesis
Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. (2019) · Applied Sciences · doi:10.3390/app9194050 · Cited by 164
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
Wang, X., Zhou, K., Wen, J.-R., & Zhao, W. X. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539382 · Cited by 164
A survey on providing customer and public administration based services using AI: chatbot
Nirala, K. K., Singh, N. K., & Purani, V. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11458-y · Cited by 181
The User Experience of ChatGPT: Findings from a Questionnaire Study of Early Users
Skjuve, M. B., Følstad, A., & Brandtzæg, P. B. (2023) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3571884.3597144 · Cited by 152
Conversational agents in business: A systematic literature review and future research directions
Bavaresco, R., Silveira, D., Reis, E., Barbosa, J., Righi, R., Costa, C., Antunes, R., Gomes, M., Gatti, C., Vanzin, M., et al. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100239 · Cited by 143
Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots
McTear, M. (2021) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02176-3 · Cited by 155
Knowledge-aware Multimodal Dialogue Systems
Liao, L., Ma, Y., He, X., Hong, R., & Chua, T. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240605 · Cited by 134
A Survey on Evaluation Methods for Chatbots
Maroengsit, W., Piyakulpinyo, T., Phonyiam, K., Pongnumkul, S., Chaovalit, P., & Theeramunkong, T. (2019) · International Conference on Information and Education Technology (ICIET) · doi:10.1145/3323771.3323824 · Cited by 133
Different Chatbots for Different Purposes: Towards a Typology of Chatbots to Understand Interaction Design
Følstad, A., Skjuve, M. B., & Brandtzæg, P. B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17705-8_13 · Cited by 142
Explaining machine learning models with interactive natural language conversations using TalkToModel
Slack, D., Krishna, S., Lakkaraju, H., & Singh, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00692-8 · Cited by 125
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
Zhang, H., Liu, Z., Xiong, C., & Liu, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.184 · Cited by 137
Communication in Human-Robot Interaction
Bonarini, A. (2020) · Current Robotics Reports · doi:10.1007/s43154-020-00026-1 · Cited by 132
Conversational transfer learning for emotion recognition
Hazarika, D., Poria, S., Zimmermann, R., & Mihalcea, R. F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.005 · Cited by 115
Evaluating Conversational Recommender Systems via User Simulation
Zhang, S., & Balog, K. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403202 · Cited by 98
S oloist : BuildingTask Bots at Scale with Transfer Learning and Machine Teaching
Peng, B., Li, C., Li, J., Shayandeh, S., Lidén, L., & Gao, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00399 · Cited by 295
IntelliBot: A Dialogue-based chatbot for the insurance industry
Nuruzzaman, M., & Hussain, O. K. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105810 · Cited by 95
Training Neural Response Selection for Task-Oriented Dialogue Systems
Henderson, M., Vulić, I., Gerz, D., Casanueva, I., Budzianowski, P., Coope, S., Spithourakis, G., Wen, T.-H., Mrkšić, N., & Su, P.-H. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1536 · Cited by 122
Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots
Izadi, S., & Forouzanfar, M. (2024) · AI · doi:10.3390/ai5020041 · Cited by 114
A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Qin, L., Xie, T., Che, W., & Liu, T. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/622 · Cited by 109
How to Approach Ambiguous Queries in Conversational Search: A Survey of Techniques, Approaches, Tools, and Challenges
Keyvan, K., & Huang, J. X. (2022) · ACM Computing Surveys · doi:10.1145/3534965 · Cited by 95
Hierarchical Variational Memory Network for Dialogue Generation
Chen, H., Ren, Z., Tang, J., Zhao, Y. E., & Yin, D. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186077 · Cited by 82
Self-citation
A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning
Pandey, S., & Sharma, S. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100198 · Cited by 100
Towards effective teaching assistants: From intent-based chatbots to LLM-powered teaching assistants
Alsafari, B., Atwell, E., Walker, A., & Callaghan, M. J. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100101 · Cited by 101
Dialogue Management in Conversational Systems: A Review of Approaches, Challenges, and Opportunities
Brabra, H., Báez, M., Benatallah, B., Gaaloul, W., Bouguelia, S., & Zamanirad, S. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3086565 · Cited by 75
A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave
Frisoni, G., Moro, G., & Carbonaro, A. (2021) · IEEE Access · doi:10.1109/access.2021.3130956 · Cited by 76
Speech Technology Progress Based on New Machine Learning Paradigm
Delić, V., Perić, Z., Sečujski, M., Jakovljevic, N. M., Nikolić, J., Mišković, D., Simić, N., Suzić, S., & Delić, T. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/4368036 · Cited by 84
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Gu, J.-C., Ling, Z.-H., & Liu, Q. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358140 · Cited by 68
The Power of Personalization: A Systematic Review of Personality-Adaptive Chatbots
Baha, T. A., Hajji, M. E., Es-Saady, Y., & Fadili, H. (2023) · SN Computer Science · doi:10.1007/s42979-023-02092-6 · Cited by 75
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
Deng, Y., Zhang, W., Xu, W., Lei, W., Chua, T., & Lam, W. P. (2022) · ACM Transactions on Information Systems · doi:10.1145/3570640 · Cited by 72
Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities
Raamkumar, A. S., & Yang, Y. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3226693 · Cited by 75
Conversational AI: An Overview of Methodologies, Applications & Future Scope
Kulkarni, P., Mahabaleshwarkar, A. S., Kulkarni, M., Sirsikar, N., & Gadgil, K. (2019) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea47591.2019.9129347 · Cited by 87
Slot Self-Attentive Dialogue State Tracking
Ye, F., Manotumruksa, J., Zhang, Q., Li, S., & Yılmaz, E. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449939 · Cited by 73
Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation
Ren, P., Chen, Z., Monz, C., Ma, J., & de Rijke, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6395 · Cited by 62
Evaluating conversational recommender systems
Jannach, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10229-x · Cited by 68
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
Yi, Z., Ouyang, J., Xu, Z., Liu, Y., Liao, T., Luo, H., & Shen, Y. (2025) · ACM Computing Surveys · doi:10.1145/3771090 · Cited by 240
Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning
Erdem, E., Kuyu, M., Yagcioglu, S., Frank, A., Parcalabescu, L., Plank, B., Babii, A., Turuta, O., Erdem, A., Calixto, I., et al. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12918 · Cited by 54
Framework for LLM applications in manufacturing
Garcia, C. I., DiBattista, M. A., Letelier, T. A., Halloran, H., & Camelio, J. A. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.030 · Cited by 57
Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations
Cai, W., & Chen, L. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3340631.3394856 · Cited by 61
User Attention-guided Multimodal Dialog Systems
Cui, C., Wang, W., Song, X., Huang, M., Xu, X.-S., & Nie, L. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331226 · Cited by 55
CRSAL
Ren, X., Yin, H., Chen, T., Wang, H., Nguyen, Q. V. H., Huang, Z. H., & Zhang, X. (2020) · ACM Transactions on Information Systems · doi:10.1145/3394592 · Cited by 54
Review of Intent Detection Methods in the Human-Machine Dialogue System
Liu, J., Li, Y., & Lin, M. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1267/1/012059 · Cited by 54
Multi-Modal Chatbot in Intelligent Manufacturing
Chen, T.-Y., Chiu, Y.-C., Bi, N., & Tsai, R. T. (2021) · IEEE Access · doi:10.1109/access.2021.3083518 · Cited by 59
Measuring perceived empathy in dialogue systems
Concannon, S. J., & Tomalin, M. (2023) · AI & Society · doi:10.1007/s00146-023-01715-z · Cited by 51
Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States
Kopp, S., & Krämer, N. C. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.580955 · Cited by 58
Exploring the Future Development of Artificial Intelligence (AI) Applications in Chatbots: A Bibliometric Analysis
Liu, L., & Duffy, V. G. (2023) · International Journal of Social Robotics · doi:10.1007/s12369-022-00956-0 · Cited by 57
Semi-Supervised Variational Reasoning for Medical Dialogue Generation
Li, D., Ren, Z., Ren, P., Chen, Z., Fan, M., Ma, J., & de Rijke, M. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462921 · Cited by 46
Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey
Balaraman, V., Sheikhalishahi, S., & Magnini, B. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.25 · Cited by 45
Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain
Meloni, A., Angioni, S., Salatino, A. A., Osborne, F., Recupero, D. R., & Motta, E. (2023) · IEEE Access · doi:10.1109/access.2023.3253388 · Cited by 50
Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities
Deng, Y., Liao, L., Lei, W., Yang, G. H., Lam, W. P., & Chua, T. (2025) · ACM Transactions on Information Systems · doi:10.1145/3715097 · Cited by 67
Korean clinical entity recognition from diagnosis text using BERT
Kim, Y.-M., & Lee, T.-H. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01241-8 · Cited by 44
Challenges and Prospects in Vision and Language Research
Kafle, K., Shrestha, R., & Kanan, C. (2019) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2019.00028 · Cited by 37
Deep context modeling for multi-turn response selection in dialogue systems
Li, L., Li, C., & Ji, D. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102415 · Cited by 40
ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots
Li, C., Zhang, X., Chrysostomou, D., & Yang, H. (2022) · IEEE Access · doi:10.1109/access.2022.3202554 · Cited by 40
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning
Kwan, W.-C., Wang, H., Wang, H., & Wong, K. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1347-y · Cited by 48
Argumentative Conversational Agents for Online Discussions
Hadfi, R., Haqbeen, J. A., Sahab, S., & Itō, T. (2021) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-021-5497-1 · Cited by 43
An Agent that Facilitates Crowd Discussion
Itō, T., Hadfi, R., & Suzuki, S. (2021) · Group Decision and Negotiation · doi:10.1007/s10726-021-09765-8 · Cited by 58
Use of chatbots for customer service in MSMEs
Cordero, J., Barba-Guamán, L., & Guamán, F. (2022) · Applied Computing and Informatics · doi:10.1108/aci-06-2022-0148 · Cited by 39
Why So Toxic?
Si, W. M., Michael Backes, A., Blackburn, J., Cristofaro, E. D., Stringhini, G., Zannettou, S., & Zhang, Y. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560599 · Cited by 52
A Reinforcement Learning Approach for Intelligent Conversational Chatbot For Enhancing Mental Health Therapy
Abubakar, A. M., Gupta, D., & Parida, S. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.087 · Cited by 40
Human-technology integration with industrial conversational agents: A conceptual architecture and a taxonomy for manufacturing
Colabianchi, S., Tedeschi, A., & Costantino, F. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100510 · Cited by 43
Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study
Adhikary, P. K., Srivastava, A., Kumar, S., Singh, S. M., Manuja, P., Gopinath, J. K., Krishnan, V., Gupta, S., Deb, K. S., & Chakraborty, T. (2024) · JMIR Mental Health · doi:10.2196/57306 · Cited by 49
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems
Razumovskaia, E., Glavaš, G., Majewska, O., Ponti, E. M., Korhonen, A., & Vulić, I. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13083 · Cited by 65
Large Language Models and Artificial Intelligence Generated Content Technologies Meet Communication Networks
Guo, J., Wang, M., Yin, H., Song, B., Chi, Y., Yu, F. R., & Yuen, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3496491 · Cited by 42
A survey on construction and enhancement methods in service chatbots design
Peng, Z., & Ma, X. (2019) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-019-00012-3 · Cited by 40
Human–machine dialogue modelling with the fusion of word- and sentence-level emotions
Peng, D., Zhou, M., Liu, C., & Ai, J. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105319 · Cited by 36
A conversational agent for querying Italian Patient Information Leaflets and improving health literacy
Minutolo, A., Damiano, E., Pietro, G. D., Fujita, H., & Esposito, M. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105004 · Cited by 41
Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact
Egger, J., Pepe, A., Gsaxner, C., Jin, Y., Li, J., & Kern, R. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.773 · Cited by 42
Reinforcement Learning-Based Dialogue Guided Event Extraction to Exploit Argument Relations
Li, Q., Peng, H. M., Li, J., Wu, J. X., Ning, Y., Wang, L., Yu, P. S., & Wang, Z. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3138670 · Cited by 36
A decision model for blockchain applicability into knowledge-based conversation system
Yang, W., Garg, S., Huang, Z., & Kang, B. H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106791 · Cited by 34
Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity
Hernandez-Bocanegra, D. C., & Ziegler, J. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579541 · Cited by 43
Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent
Tang, F., Uchendu, I., Wang, F., Dodge, H. H., & Zhou, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-61994-0 · Cited by 35
Context-Controlled Topic-Aware Neural Response Generation for Open-Domain Dialog Systems
Ling, Y., Cai, F., Hu, X., Liu, J., Chen, W., & Chen, H. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102392 · Cited by 33
Self-citation
Developing a Task-Based Dialogue System for English Language Learning
Li, K.-C., Chang, M., & Wu, K.-H. (2020) · Education Sciences · doi:10.3390/educsci10110306 · Cited by 38
Multimodal Dialog System: Relational Graph-based Context-aware Question Understanding
Zhang, H., Liu, M., Gao, Z., Lei, X., Wang, Y., & Nie, L. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475234 · Cited by 33
How to compete with robots by assessing job automation risks and resilient alternatives
Paolillo, A., Colella, F., Nosengo, N., Schiano, F., Stewart, W., Zambrano, D., Chappuis, I., Lalive, R., & Floreano, D. (2022) · Science Robotics · doi:10.1126/scirobotics.abg5561 · Cited by 38
Can AI chatbots effectively improve EFL learners’ learning effects?—A meta-analysis of empirical research from 2022–2024
Liu, Z., Zhang, W., & Yang, P. (2025) · Computer Assisted Language Learning · doi:10.1080/09588221.2025.2456512 · Cited by 42
Dialogue Enhanced Extended Reality: Interactive System for the Operator 4.0
Serras, M., García-Sardiña, L., Simões, B., Álvarez, H., & Arambarri, J. (2020) · Applied Sciences · doi:10.3390/app10113960 · Cited by 31
I am Robot, Your Health Adviser for Older Adults: Do You Trust My Advice?
Giorgi, I., Minutolo, A., Tirotto, F. A., Hagen, O., Esposito, M., Gianni, M., Palomino, M. A., & Masala, G. L. (2023) · International Journal of Social Robotics · doi:10.1007/s12369-023-01019-8 · Cited by 32
Deep learning applications in games: a survey from a data perspective
Hu, Z., Ding, Y., Wu, R., Li, L., Zhang, R., Hu, Y., Qiu, F., Zhang, Z., Wang, K., Zhao, S., et al. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05094-2 · Cited by 33
Retracted: Generative model chatbot for Human Resource using Deep Learning
Sheikh, S. A., Tiwari, V., & Singhal, S. (2019) · International Conference on Data Science and Engineering · doi:10.1109/icdse47409.2019.8971795 · Cited by 31
Retracted
Survey of Textbased Chatbot in Perspective of Recent Technologies
Borah, B., Pathak, D., Sarmah, P., Som, B., & Nandi, S. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-8581-0_7 · Cited by 32
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Gu, J.-C., Ling, Z.-H., & Liu, Q. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2955290 · Cited by 30
KIDE4I: A Generic Semantics-Based Task-Oriented Dialogue System for Human-Machine Interaction in Industry 5.0
Aceta, C., Fernández, I., & Soroa, A. (2022) · Applied Sciences · doi:10.3390/app12031192 · Cited by 29

Authors: Chen, H., Liu, X., Yin, D., & Tang, J.
Affiliations: Jingdong (China) (CN)
Michigan State University (US)
ORCID: Chen, Hongshen
Liu, Xiaorui
Yin, Dawei
Tang, Jiliang
Venue: ACM SIGKDD Explorations Newsletter
Publisher: Association for Computing Machinery (ACM)
Published: 2017-11-21
Volume: 19
Issue: 2
Pages: 25-35
ISSN: 1931-0145 (ISSN-L)
1931-0153
DOI: 10.1145/3166054.3166058
OpenAlex: W4290742115
Cited by: 579
References: 134
Citations by year: 2026: 28 · 2025: 64 · 2024: 77 · 2023: 104 · 2022: 97 · 2021: 83 · 2020: 55 · 2019: 56 · 2018: 15
FWCI: 20.57
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM SIGKDD Explorations Newsletter (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: dialogue systems, deep learning, task-oriented dialogue systems, response generation, feature representation learning, chatbots

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Survey on Dialogue Systems: Recent Advances and New Frontiers

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks