Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

Adam: A Method for Stochastic Optimization

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Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

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Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

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A Persona-Based Neural Conversation Model

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The Second Dialog State Tracking Challenge

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Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding

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Learning End-to-End Goal-Oriented DialogPreprint

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A Simple Language Model for Task-Oriented DialoguePreprint

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Survey on evaluation methods for dialogue systems

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A survey on empathetic dialogue systems

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ConveRT: Efficient and Accurate Conversational Representations from Transformers

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Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

Hancock, B., Bordes, A., Mazare, P.-E., & Weston, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1358 · Cited by 161

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Trakadas, P. T., Simoens, P., Gkonis, P. K., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., Skármeta, A., Trochoutsos, C., Calvο, D., Lobo, T. P., et al. (2020) · Sensors · doi:10.3390/s20195480 · Cited by 155

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

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GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection

He, W., Dai, Y., Zheng, Y., Wu, Y., Cao, Z., Liu, D., Jiang, P., Yang, M., Huang, F., Si, L., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21320 · Cited by 121

Neural Approaches to Conversational AI

Gao, J., Galley, M., & Li, L. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835533 · Cited by 125

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & Castro da Silva, B. (2026) · ACM Computing Surveys · doi:10.1145/3743127 · Cited by 92

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Yasunaga, M., & Liang, P. (2020) · arXiv · doi:10.48550/arxiv.2005.10636 · Cited by 81

CRSAL

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Putting Humans in the Natural Language Processing Loop: A SurveyPreprint

Wang, Z. J., Choi, D., Xu, S., & Yang, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04044 · Cited by 82

Emotion recognition in conversations with emotion shift detection based on multi-task learning

Gao, Q., Cao, B., Guan, X., Gu, T., Bao, X., Wu, J., Liu, B., & Cao, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108861 · Cited by 55

Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations

Coope, S., Farghly, T., Gerz, D., Vulić, I., & Henderson, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.11 · Cited by 56

MOSS: End-to-End Dialog System Framework with Modular Supervision

Liang, W., Tian, Y., Chen, C., & Yu, Z. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6349 · Cited by 43

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

Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation

Fernandes, P., Madaan, A., Liu, E., Farinhas, A., Martins, P. H., Bertsch, A., de Souza, J. G. C., Zhou, S., Wu, T., Neubig, G., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00626 · Cited by 94

Human-centric dialog training via offline reinforcement learning

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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

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

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

Budgeted Policy Learning for Task-Oriented Dialogue Systems

Zhang, Z., Li, X., Gao, J., & Chen, E. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1364 · Cited by 36

A knowledge infused context driven dialogue agent for disease diagnosis using hierarchical reinforcement learning

Tiwari, A., Saha, S., & Bhattacharyya, P. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108292 · Cited by 34

Multimodal Conversational AI: A Survey of Datasets and Approaches

Sundar, A., & Heck, L. P. (2022) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2022.nlp4convai-1.12 · Cited by 42

End-to-End Learning of Task-Oriented Dialogs

Liu, B., & Lane, I. R. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n18-4010 · Cited by 32

Adversarial Learning of Task-Oriented Neural Dialog Models

Liu, B., & Lane, I. R. (2018) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w18-5041 · Cited by 32

Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability

Kamuni, N., Shah, H., Chintala, S., Kunchakuri, N., & Alla, S. (2024) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc59802.2024.00063 · Cited by 36

Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment

Dai, Y., Li, H., Tang, C., Li, Y., Sun, J. Q., & Zhu, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.57 · Cited by 27

A review of dialogue systems: current trends and future directions

Algherairy, A. S., & Ahmed, M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09322-1 · Cited by 25

Building an Arabic Flight Booking Dialogue System Using a Hybrid Rule-Based and Data Driven Approach

Al-Ajmi, A.-H., & Al-Twairesh, N. (2021) · IEEE Access · doi:10.1109/access.2021.3049732 · Cited by 24

Minimizing Hallucinations and Communication Costs: Adversarial Debate and Voting Mechanisms in LLM-Based Multi-Agents

Yang, Y., Ma, Y., Feng, H., Cheng, Y., & Han, Z. (2025) · Applied Sciences · doi:10.3390/app15073676 · Cited by 29

KETOD: Knowledge-Enriched Task-Oriented Dialogue

Chen, Z., Liu, B., Moon, S., Sankar, C., Crook, P., & Wang, W. Y. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.197 · Cited by 23

Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI

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Learning Dialog Policies from Weak Demonstrations

Gordon-Hall, G., Gorinski, P. J., & Cohen, S. B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.129 · Cited by 26

Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration

Shi, W., Li, Y., Sahay, S., & Yu, Z. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.295 · Cited by 25

DRESS : Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback

Chen, Y., Sikka, K., Cogswell, M., Ji, H., & Divakaran, A. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01350 · Cited by 56

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

Qin, L., Pan, W., Chen, Q., Liao, L., Zhou, Y., Zhang, Y., Che, W., & Li, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.363 · Cited by 39

Improving Factual Consistency Between a Response and Persona Facts

Mesgar, M., Simpson, E., & Gurevych, I. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.44 · Cited by 31

Flexibly-Structured Model for Task-Oriented Dialogues

Shu, L., Molino, P., Namazifar, M., Xu, H., Liu, B., Zheng, H., & Tür, G. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5922 · Cited by 20

Survey on dialogue systems including slavic languages

Wołk, K., Wołk, A., Wnuk, D. C., Grześ, T., & Skubis, I. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.076 · Cited by 21

Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management

Gritta, M., Λάμπουρας, Γ., & Iacobacci, I. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00352 · Cited by 18

TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling

Xie, H., Liu, Z., Xiong, C., Liu, Z., & Copestake, A. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.145 · Cited by 23

Enhancing sentient embodied conversational agents with machine learning

Tellols, D., López-Sánchez, M., Rodríguez, I., Almajano, P., & Puig, A. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.035 · Cited by 17

A Survey on Dialog Management: Recent Advances and ChallengesPreprint

Dai, Y., Yu, H., Jiang, Y., Tang, C., Li, Y., & Sun, J. Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02233 · Cited by 24

CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Verma, S. S., Fu, J., Yang, S., & Levine, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.332 · Cited by 16

Multi-Action Dialog Policy Learning with Interactive Human Teaching

Jhunjhunwala, M., Bryant, C., & Shah, P. (2020) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2020.sigdial-1.36 · Cited by 14

Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances

Falke, T., Boese, M., Sorokin, D., Tırkaz, Ç., & Lehnen, P. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-industry.3 · Cited by 14

The Why and The How: A Survey on Natural Language Interaction in Visualization

Voigt, H., Alaçam, Ö., Meuschke, M., Lawonn, K., & Zarrieß, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.27 · Cited by 14

Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use

Rajendran, J., Ganhotra, J., & Polymenakos, L. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00274 · Cited by 14

Sentence encoding for Dialogue Act classification

Duran, N., Battle, S., & Smith, J. (2021) · Natural Language Engineering · doi:10.1017/s1351324921000310 · Cited by 13

IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

Li, C., Chrysostomou, D., Zhang, X., & Yang, H. (2023) · IEEE Access · doi:10.1109/access.2023.3259325 · Cited by 13

Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning

Yu, X., Chen, M., & Yu, Z. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.439 · Cited by 49

The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

Kirk, H. R., Bean, A., Vidgen, B., Röttger, P., & Hale, S. A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.148 · Cited by 48

Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems.Preprint

Razumovskaia, E., Glavaš, G., Majewska, O., Korhonen, A., & Vulić, I. (2021) · arXiv (Cornell University) · Cited by 13

Hierarchical Reinforcement Learning for Open-Domain Dialog

Saleh, A., Jaques, N., Ghandeharioun, A., Shen, J. H., & Picard, R. W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6400 · Cited by 16

Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting

Lu, Y., Srivastava, M., Kramer, J., Elfardy, H., Kahn, A., Wang, S., & Bhardwaj, V. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-2007 · Cited by 12

Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues

Paul, S., Goel, R., & Hakkani-Tür, D. Z. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1866 · Cited by 12

Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy

Zhao, Y., Wang, Z., Zhu, C., & Wang, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.354 · Cited by 12

Break, Repair, Learn, Break Less: Investigating User Preferences for Assignment of Divergent Phrasing Learning Burden in Human-Agent Interaction to Minimize Conversational Breakdowns

Foosherian, M., Freire, S. K., Niforatos, E., Hribernik, K., & Thoben, K. (2022) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3568444.3568454 · Cited by 11

Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management

Saito, A. (2018) · doi:10.18653/v1/w18-5707 · Cited by 10

SUMBT+LaRL: End-to-end Neural Task-oriented Dialog System with Reinforcement LearningPreprint

Lee, H., Jo, S., Kim, H., Jung, S., & Kim, T.-Y. (2020) · arXiv (Cornell University) · Cited by 10

SUMBT+LaRL: Effective Multi-Domain End-to-End Neural Task-Oriented Dialog System

Lee, H., Jo, S., Kim, H., Jung, S., & Kim, T.-Y. (2021) · IEEE Access · doi:10.1109/access.2021.3105461 · Cited by 9

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems

Snell, C., Yang, S., Fu, J., Su, Y., & Levine, S. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.181 · Cited by 27

A Survey on the Recent Advancements in Human-Centered Dialog Systems

Oruche, R., Goruganthu, S. K., Akula, R., Cheng, X., Goni, M. A., Shibo, B. W., Kee, K. F., Zampieri, M., & Calyam, P. P. (2025) · ACM Computing Surveys · doi:10.1145/3729220 · Cited by 11

Deep Learning for Dialogue Systems

Chen, Y.-N., Çelikyılmaz, A., & Hakkani-Tür, D. Z. (2018) · International Conference on Computational Linguistics · Cited by 8

Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior

Kojima, N., Suhr, A., & Artzi, Y. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00428 · Cited by 13

How to Tame Your Data: Data Augmentation for Dialog State Tracking

Summerville, A., Hashemi, J., Ryan, J. O., & ferguson, W. (2020) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2020.nlp4convai-1.4 · Cited by 9

Resume Scanning and Emotion Recognition System based on Machine Learning Algorithms

Vishruth, R. G., Sunitha, R., Varuna, K. S., Varshini, N., & Honnavalli, P. B. (2020) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca49313.2020.9297586 · Cited by 8

Interacting with Non-Cooperative User

Lei, W., Zhang, Y., Song, F., Liang, H., Mao, J., Lv, J., Yang, Z., & Chua, T. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532001 · Cited by 18

Reinforced Approximate Exploratory Data Analysis

Garg, S., Mitra, S., Yu, T., Gadhia, Y., & Kashettiwar, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25929 · Cited by 8

Dealing with Semantic Underspecification in Multimodal NLP

Pezzelle, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.675 · Cited by 14

Scaling up chatbots for corporate service delivery systems

Barros, A., Sindhgatta, R., & Nili, A. (2021) · Communications of the ACM · doi:10.1145/3446912 · Cited by 8

Convolutional Neural Networks for Dialogue State Tracking without Pre-Trained Word Vectors or Semantic Dictionaries

Korpusik, M., & Glass, J. R. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639559 · Cited by 7

Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning

Campos, J. A., Cho, K., Otegi, A., Soroa, A., Agirre, E., & Azkune, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.230 · Cited by 9

Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems

Wu, Y.-C., Tseng, B.-H., & Gašić, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.75 · Cited by 7

Dialogue Management for Interactive API Search

Eberhart, Z., & McMillan, C. (2021) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme52107.2021.00031 · Cited by 7

Affective Decoding for Empathetic Response Generation

Zeng, C., Chen, G., Lin, C., Li, R., & Chen, Z. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.37 · Cited by 7

Adaptive Global-Local Context Fusion for Multi-Turn Spoken Language Understanding

Tran, T., Wei, K., Ruan, W., McGowan, R., Susanj, N., & Strimel, G. P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21536 · Cited by 7

Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability

Jiang, Z., Mao, X.-L., Huang, Z., Ma, J., & Li, S. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5918 · Cited by 6

Generating Persona-Consistent Dialogue Responses Using Deep Reinforcement LearningPreprint

Mesgar, M., Simpson, E., Wang, Y., & Gurevych, I. (2020) · arXiv (Cornell University) · Cited by 6

Chatbot Based on Emotions Using Deep Learning

Vakkala, N., Renuka, G. B., Naga Lakshmi, B., P, S. K. C., Rudra Sekhar Reddy, A., & Paramesh, J. (2023) · International Conference on Optimization Techniques for Learning (ICOTL) · doi:10.1109/icotl59758.2023.10435281 · Cited by 5

Multimodal Reinforcement Learning for Robots Collaborating with Humans

Shervedani, A. M., Li, S., Monaikul, N., Abbasi, B., Žefran, M., & Eugenio, B. D. (2025) · International Journal of Social Robotics · doi:10.1007/s12369-025-01287-6 · Cited by 5

SIM: A Slot-Independent Neural Model for Dialogue State Tracking

Zhu, C., Zeng, M., & Huang, X. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5905 · Cited by 5

M.I.N.I.-KID interviews with adolescents: a corpus-based language analysis of adolescents with depressive disorders and the possibilities of continuation using Chat GPT

Jarvers, I., Ecker, A., Donabauer, P., Kampa, K., Weißenbacher, M., Schleicher, D., Kandsperger, S., Brunner, R. M., & Ludwig, B. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1425820 · Cited by 4

Utterance Alignment of Language Models for Effective User Simulation in Task-Oriented Dialogues

Luo, X., Wang, J., & Zhang, X. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3525981 · Cited by 4

Bother Me: Understanding Learner Perceptions and Behaviors with Virtual Users in Interview Training

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An Evaluation of Chinese Human-Computer Dialogue Technology

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