A Survey on Dialogue Systems: Recent Advances and New Frontiers

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

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 · see citing works

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 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works

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 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

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 · see citing works

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works

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 · see citing works

Deep Generative Image Models using a Laplacian Pyramid of Adversarial NetworksPreprint

Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015) · Neural Information Processing Systems · doi:10.48550/arxiv.1506.05751 · Cited by 2,360 · see citing works

Generating Sentences from a Continuous Space

Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1002 · Cited by 2,061 · see citing works

Learning deep structured semantic models for web search using clickthrough data

Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,054 · see citing works

A Diversity-Promoting Objective Function for Neural Conversation Models

Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1014 · Cited by 1,981 · see citing works

Semi-Supervised Learning with Deep Generative Models

Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. (2014) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1406.5298 · Cited by 1,646 · see citing works

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

Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.9883 · Cited by 1,639 · see citing works

Computing Machinery and Intelligence

Turing, A. M. (2007) · Springer eBooks · doi:10.1007/978-1-4020-6710-5_3 · Cited by 1,439 · see citing works

A Neural Conversational Model

Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · see citing works

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

Liu, C., Lowe, R., Serban, I. V., Noseworthy, M., Charlin, L., & Pineau, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1230 · Cited by 1,262 · see citing works

Deep Reinforcement Learning for Dialogue Generation

Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., & Gao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1127 · Cited by 1,221 · see citing works

Comparing Twitter and Traditional Media Using Topic Models

Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20161-5_34 · Cited by 1,214 · see citing works

Convolutional Neural Network Architectures for Matching Natural Language SentencesPreprint

Hu, B., Lu, Z., Li, H., & Chen, Q. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03244 · Cited by 1,168 · see citing works

Untitled work

(2016) · doi:10.18653/v1/d16-1147 · Cited by 1,035 · see citing works

Neural Responding Machine for Short-Text Conversation

Shang, L., Lu, Z., & Li, H. (2015) · doi:10.3115/v1/p15-1152 · Cited by 1,009 · see citing works

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Williams, R. J. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3618-5_2 · Cited by 941 · see citing works

A Persona-Based Neural Conversation Model

Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1094 · Cited by 898 · see citing works

Adversarial Learning for Neural Dialogue Generation

Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1230 · Cited by 880 · see citing works

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Wen, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1199 · Cited by 876 · see citing works

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Ambati, B. R., Deoskar, T., Johnson, M., & Steedman, M. J. (2015) · The Association for Computational Linguistics · Cited by 860 · see citing works

Maximum mutual information estimation of hidden Markov model parameters for speech recognition

Bahl, L., Brown, P., de Souza, P., & Mercer, R. (2005) · ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1986.1169179 · Cited by 817 · see citing works

A Network-based End-to-End Trainable Task-oriented Dialogue System

Wen, T.-H., Vandyke, D., Mrkšić, N., Gašić, M., Rojas-Barahona, L. M., Su, P.-H., Ultes, S., & Young, S. J. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1042 · Cited by 807 · see citing works

Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

Zhou, H. M., Huang, M., Zhang, T., Zhu, X., & Liu, B. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11325 · Cited by 786 · see citing works

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J., Gao, J., & Dolan, B. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1020 · Cited by 768 · see citing works

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Serban, I. V., Sordoni, A., Lowe, R., Charlin, L., Pineau, J., Courville, A. C., & Bengio, Y. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v31i1.10983 · Cited by 734 · see citing works

Learning semantic representations using convolutional neural networks for web search

Shen, Y., He, X., Gao, J., Deng, L., & Mesnil, G. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2577348 · Cited by 677 · see citing works

Addressing the Rare Word Problem in Neural Machine Translation

Luong, M.-T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2015) · doi:10.3115/v1/p15-1002 · Cited by 626 · see citing works

Data-Driven Response Generation in Social Media

Ritter, A., Cherry, C. A., & Dolan, W. B. (2011) · Cited by 566 · see citing works

PARADISE

Walker, M. A., Litman, D. J., Kamm, C., & Abella, A. (1997) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/976909.979652 · Cited by 548 · see citing works

Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots

Wu, Y., Wu, W., Chen, X., Zhou, M., & Li, Z. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1046 · Cited by 511 · see citing works

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Papernot, N., Abadi, M., Erlingsson, Ú., Goodfellow, I., & Talwar, K. (2016) · arXiv · doi:10.48550/arxiv.1610.05755 · Cited by 492 · see citing works

The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management

Young, S. J., Gašić, M., Keizer, S., Mairesse, F., Schatzmann, J., Thomson, B., & Yu, K. (2009) · Computer Speech & Language · doi:10.1016/j.csl.2009.04.001 · Cited by 481 · see citing works

Learning End-to-End Goal-Oriented DialogPreprint

Bordes, A., Boureau, Y.-L., & Weston, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07683 · Cited by 481 · see citing works

Neural Belief Tracker: Data-Driven Dialogue State Tracking

Mrkšić, N., Séaghdha, D. Ó., Wen, T.-H., Thomson, B., & Young, S. J. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1163 · Cited by 460 · see citing works

A Knowledge-Grounded Neural Conversation Model

Ghazvininejad, M., Brockett, C., Chang, M.-W., Dolan, B., Gao, J., Yih, W.-T., & Galley, M. (2018) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v32i1.11977 · Cited by 457 · see citing works

Topic Aware Neural Response Generation

Chen, X., Wu, W., Wu, Y., Liu, J., Huang, Y., Zhou, M., & Ma, W. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10981 · Cited by 448 · see citing works

On the semantics and pragmatics of linguistic feedback

ALLWOOD, J., NIVRE, J., & AHLSÉN, E. (1992) · Journal of Semantics · doi:10.1093/jos/9.1.1 · Cited by 442 · see citing works

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Yarowsky, D., Baldwin, T., Korhonen, A., Livescu, K., & Bethard, S. J. (2013) · Empirical Methods in Natural Language Processing · Cited by 422 · see citing works

Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding

Mesnil, G., He, X., Deng, L., & Bengio, Y. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-596 · Cited by 419 · see citing works

Key-Value Retrieval Networks for Task-Oriented Dialogue

Eric, M., Krishnan, L., Charette, F., & Manning, C. D. (2017) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w17-5506 · Cited by 397 · see citing works

Phrase-Based Statistical Machine Translation

Zens, R., Och, F. J., & Ney, H. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45751-8_2 · Cited by 354 · see citing works

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsPreprint

Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., & Batra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02424 · Cited by 350 · see citing works

Deal or No Deal? End-to-End Learning of Negotiation Dialogues

Lewis, M., Yarats, D., Dauphin, Y., Parikh, D., & Batra, D. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1259 · Cited by 347 · see citing works

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Lowe, R., Noseworthy, M., Serban, I. V., Angelard-Gontier, N., Bengio, Y., & Pineau, J. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1103 · Cited by 341 · see citing works

Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

Yan, R., Song, Y., & Wu, H. R. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2911542 · Cited by 338 · see citing works

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

Williams, J. D., Asadi, K., & Zweig, G. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1062 · Cited by 325 · see citing works

Spoken language understanding using long short-term memory neural networks

Yao, K., Peng, B., Zhang, Y., Yu, D., Zweig, G., & Shi, Y. (2014) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078572 · Cited by 324 · see citing works

Recurrent neural networks for language understanding

Yao, K., Zweig, G., Hwang, M.-Y., Shi, Y., & Yu, D. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-569 · Cited by 320 · see citing works

The Dialog State Tracking Challenge

Williams, J. D., Raux, A., Ramachandran, D., & Black, A. W. (2013) · Cited by 315 · see citing works

Topic Aware Neural Response GenerationPreprint

Chen, X., Wu, W., Wu, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.08340 · Cited by 313 · see citing works

The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

Lowe, R., Pow, N., Serban, I. V., & Pineau, J. (2015) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w15-4640 · Cited by 282 · see citing works

Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

Dhingra, B., Li, L. C., Li, X., Gao, J., Chen, Y.-N., Ahmed, F., & Deng, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1045 · Cited by 274 · see citing works

A Simple, Fast Diverse Decoding Algorithm for Neural GenerationPreprint

Li, J., Monroe, W., & Jurafsky, D. (2016) · arXiv.org · doi:10.48550/arxiv.1611.08562 · Cited by 261 · see citing works

End-to-End Task-Completion Neural Dialogue SystemsPreprint

Li, X., Chen, Y.-N., Li, L. C., Gao, J., & Çelikyılmaz, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01008 · Cited by 259 · see citing works

The Acoustic-Modeling Problem in Automatic Speech Recognition.

Brown, P. F. (1987) · doi:10.21236/ada188529 · Cited by 248 · see citing works

A Diversity-Promoting Objective Function for Neural Conversation ModelsPreprint

Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.03055 · Cited by 242 · see citing works

A Deep Architecture for Matching Short Texts

Lu, Z., & Li, H. (2013) · Cited by 230 · see citing works

Multi-view Response Selection for Human-Computer Conversation

Zhou, X., Dong, D., Wu, H. R., Zhao, S., Yu, D., Tian, H., Liu, X., & Yan, R. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1036 · Cited by 230 · see citing works

Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text ConversationPreprint

Mou, L., Song, Y., Yan, R., Li, G., Zhang, L., & Jin, Z. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.00970 · Cited by 214 · see citing works

RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

Tao, C., Mou, L., Zhao, D., & Yan, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11321 · Cited by 213 · see citing works

Affective Neural Response Generation

Asghar, N., Poupart, P., Hoey, J., Jiang, X., & Mou, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-76941-7_12 · Cited by 203 · see citing works

An Information Retrieval Approach to Short Text ConversationPreprint

Ji, Z., Lu, Z., & Li, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.6988 · Cited by 202 · see citing works

AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine

Qiu, M., Li, F.-L., Wang, S., Gao, X., Chen, Y., Zhao, W., Chen, H., Huang, J., & Chu, W. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2079 · Cited by 197 · see citing works

A Deep Reinforcement Learning ChatbotPreprint

Serban, I. V., Sankar, C., Germain, M., Zhang, S., Lin, Z., Subramanian, S., Kim, T., Pieper, M., Chandar, S., Ke, N. R., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02349 · Cited by 195 · see citing works

Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

Zhao, T., & Eskénazi, M. (2016) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w16-3601 · Cited by 192 · see citing works

Hierarchical Recurrent Attention Network for Response Generation

Chen, X., Wu, Y., Wu, W., Huang, Y., & Zhou, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11965 · Cited by 190 · see citing works

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

(2015) · doi:10.3115/v1/p15-2 · Cited by 186 · see citing works

Neural Generative Question Answering

Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., & Li, X. (2016) · Proceedings of the Workshop on Human-Computer Question Answering · doi:10.18653/v1/w16-0106 · Cited by 186 · see citing works

A Dataset for Research on Short-Text Conversations

Wang, H., Lu, Z., Li, H., & Chen, E. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1096 · Cited by 185 · see citing works

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

Serban, I. V., Klinger, T., Tesauro, G. J., Talamadupula, K., Zhou, B., Bengio, Y., & Courville, A. C. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10984 · Cited by 178 · see citing works

PARADISE: A Framework for Evaluating Spoken Dialogue AgentsPreprint

Walker, M. A., Litman, D. J., Kamm, C., & Abella, A. (1997) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9704004 · Cited by 175 · see citing works

Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

Wen, T.-H., Gašić, M., Mrkšić, N., Rojas-Barahona, L. M., Su, P.-H., Vandyke, D., & Young, S. J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1015 · Cited by 168 · see citing works

Building Task-Oriented Dialogue Systems for Online Shopping

Yan, Z., Duan, N., Chen, P., Zhou, M., Zhou, J., & Li, Z. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11182 · Cited by 164 · see citing works

A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information

Wang, Z., & Lemon, O. (2013) · doi:10.13140/2.1.1213.1522 · Cited by 159 · see citing works

Trainable sentence planning for complex information presentation in spoken dialog systems

Stent, A. J., Prasad, R., & Walker, M. A. (2004) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1218955.1218966 · Cited by 154 · see citing works

Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

Wen, T.-H., Gašić, M., Kim, D., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. J. (2015) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w15-4639 · Cited by 152 · see citing works

Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation

Qian, Q., Huang, M., Zhao, H., Xu, J., & Zhu, X. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/595 · Cited by 142 · see citing works

Deep Neural Network Approach for the Dialog State Tracking Challenge

Henderson, M., Thomson, B., & Young, S. J. (2013) · Cited by 134 · see citing works

How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models

Tian, Z., Yan, R., Mou, L., Song, Y., Feng, Y., & Zhao, D. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2036 · Cited by 128 · see citing works

Evaluating Evaluation Methods for Generation in the Presence of Variation

Stent, A., Marge, M., & Singhai, M. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30586-6_38 · Cited by 126 · see citing works

Deep belief nets for natural language call-routing

Sarikaya, R., Hinton, G. E., & Ramabhadran, B. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947649 · Cited by 126 · see citing works

Towards deeper understanding: Deep convex networks for semantic utterance classification

Tür, G., Deng, L., Hakkani-Tür, D. Z., & He, X. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6289054 · Cited by 123 · see citing works

Web-style ranking and SLU combination for dialog state tracking

Williams, J. D. (2014) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/v1/w14-4339 · Cited by 117 · see citing works

RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog SystemsPreprint

Tao, C., Mou, L., Zhao, D., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.03079 · Cited by 117 · see citing works

Emotional Chatting Machine: Emotional Conversation Generation with Internal and External MemoryPreprint

Zhou, H., Huang, M., Zhang, T., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01074 · Cited by 114 · see citing works

Hierarchical Recurrent Attention Network for Response GenerationPreprint

Chen, X., Wu, W., Wu, Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.07149 · Cited by 114 · see citing works

A form-based dialogue manager for spoken language applications

Goddeau, D., Meng, H. M. L., Polifroni, J. H., Seneff, S., & Busayapongchai, S. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607458 · Cited by 113 · see citing works

End-to-end LSTM-based dialog control optimized with supervised and reinforcement learningPreprint

Williams, J. D., & Zweig, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01269 · Cited by 112 · see citing works

Use of kernel deep convex networks and end-to-end learning for spoken language understanding

Deng, L., Tür, G., He, X., & Hakkani-Tür, D. Z. (2012) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2012.6424224 · Cited by 111 · see citing works

A Conditional Variational Framework for Dialog Generation

Shen, X., Su, H., Li, Y., Li, W., Niu, S., Zhao, Y., Aizawa, A., & Long, G. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2080 · Cited by 106 · see citing works

Personalizing a Dialogue System With Transfer Reinforcement Learning

Mo, K., Zhang, Y., Li, S., Li, J., & Yang, Q. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11938 · Cited by 103 · see citing works

Neural personalized response generation as domain adaptation

Zhang, W., Zhu, Q., Wang, Y., Zhao, Y., & Liu, T. (2018) · World Wide Web · doi:10.1007/s11280-018-0598-6 · Cited by 94 · see citing works

Training a sentence planner for spoken dialogue using boosting

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Dialogue Learning With Human-In-The-Loop

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Deep context modeling for multi-turn response selection in dialogue systems

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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 · see citing works

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 39 · see citing works

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 · see citing works

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 46 · see citing works

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 · see citing works

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 47 · see citing works

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 38 · see citing works

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 39 · see citing works

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 41 · see citing works

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 57 · see citing works

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 38 · see citing works

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 37 · see citing works

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 · see citing works

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 36 · see citing works

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 41 · see citing works

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 35 · see citing works

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 · see citing works

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 33 · see citing works

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 33 · see citing works

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 · see citing works

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 36 · see citing works

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 · see citing works

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 35 · see citing works

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 39 · see citing works

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 · see citing works

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 30 · see citing works

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 · see citing works

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 30 · see citing works

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 29 · see citing works

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 · see citing works

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 279 · see citing works

Deep Learning for Dialogue Systems

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

A Study on the Dialogue System based on Deep Neural Network

교양학부, 한., Jhee, I.-Y., & Kim, H. (2018) · Lingua Humanitatis · doi:10.16945/2018202012

심층학습을 이용한 대화시스템의 연구방법 분석

지인영, & 김희동 (2018) · 인문언어

Recent Advances in Deep Learning-based Dialogue Systems

Ni, J., Young, T., Pandelea, V., Xue, F., Adiga, V. A. K., & Cambria, E. (2021) · Cited by 4 · see citing works