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
- J. Allwood, J. Nivre, and E. Ahlsén. On the semantics and pragmatics of linguistic feedback. Journal of semantics, 9(1):1--26, 1992.Google ScholarCross Ref
- D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.Google Scholar
- L. Bahl, P. Brown, P. De Souza, and R. Mercer. Maximum mutual information estimation of hidden markov model parameters for speech recognition. In Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP'86., volume 11, pages 49--52. IEEE, 1986.Google Scholar
- A. Bordes, Y. L. Boureau, and J. Weston. Learning end-to-end goal-oriented dialog. 2017.Google Scholar
- P. F. Brown. The acoustic-modeling problem in automatic speech recognition. Technical report, CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE, 1987.Google Scholar
- K. Cao and S. Clark. Latent variable dialogue models and their diversity. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 182--187, Valencia, Spain, April 2017. Association for Computational Linguistics.Google ScholarCross Ref
- K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724--1734, Doha, Qatar, October 2014. Association for Computational Linguistics.Google ScholarCross Ref
- S. Choudhary, P. Srivastava, L. Ungar, and J. Sedoc. Domain aware neural dialog system. 2017.Google Scholar
- H. Cuayhuitl, S. Keizer, and O. Lemon. Strategic dialogue management via deep reinforcement learning. arxiv.org, 2015.Google Scholar
- L. Deng, G. Tur, X. He, and D. Hakkani-Tur. Use of kernel deep convex networks and end-to-end learning for spoken language understanding. In Spoken Language Technology Workshop (SLT), 2012 IEEE, pages 210--215. IEEE, 2012.Google ScholarCross Ref
- A. Deoras and R. Sarikaya. Deep belief network based semantic taggers for spoken language understanding. 2013.Google Scholar
- B. Dhingra, L. Li, X. Li, J. Gao, Y.-N. Chen, F. Ahmed, and L. Deng. Towards end-to-end reinforcement learning of dialogue agents for information access. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 484--495, Vancouver, Canada, July 2017. Association for Computational Linguistics.Google ScholarCross Ref
- O. Dušek and F. Jurcicek. A context-aware natural language generator for dialogue systems. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 185--190, Los Angeles, September 2016. Association for Computational Linguistics.Google ScholarCross Ref
- O. Dušek and F. Jurcicek. Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 45--51, Berlin, Germany, August 2016. Association for Computational Linguistics.Google ScholarCross Ref
- M. Eric and C. D. Manning. Key-value retrieval networks for task-oriented dialogue. arXiv preprint arXiv:1705.05414, 2017.Google Scholar
- M. Ghazvininejad, C. Brockett, M. W. Chang, B. Dolan, J. Gao, W. Yih, and M. Galley. A knowledge-grounded neural conversation model. 2017.Google Scholar
- D. Goddeau, H. Meng, J. Polifroni, S. Sene, and S. Busayapongchai. A form-based dialogue manager for spoken language applications. In Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, volume 2, pages 701--704. IEEE, 1996.Google ScholarCross Ref
- A. Graves. Long short-term memory. Neural Computation, 9(8):1735, 1997.Google ScholarDigital Library
- H. B. Hashemi, A. Asiaee, and R. Kraft. Query intent detection using convolutional neural networks.Google Scholar
- M. Henderson, B. Thomson, and S. Young. Deep neural network approach for the dialog state tracking challenge. In Proceedings of the SIGDIAL 2013 Conference, pages 467--471, 2013.Google Scholar
- B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neural network architectures for matching natural language sentences. In Advances in neural information processing systems, pages 2042--2050, 2014. Google ScholarDigital Library
- P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 2333--2338. ACM, 2013. Google ScholarDigital Library
- Z. Ji, Z. Lu, and H. Li. An information retrieval approach to short text conversation. arXiv preprint arXiv:1408.6988, 2014.Google Scholar
- C. Kamm. User interfaces for voice applications. Proceedings of the National Academy of Sciences, 92(22):10031--10037, 1995.Google ScholarCross Ref
- D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling. Semi-supervised learning with deep generative models. Advances in Neural Information Pro- cessing Systems, 4:3581--3589, 2014. Google ScholarDigital Library
- D. P. Kingma and M. Welling. Auto-encoding variational bayes. In ICLR, 2014.Google Scholar
- S. Lee. Structured discriminative model for dialog state tracking. In SIGDIAL Conference, pages 442--451, 2013.Google Scholar
- S. Lee and M. Eskenazi. Recipe for building robust spoken dialog state trackers: Dialog state tracking challenge system description. In SIGDIAL Conference, pages 414--422, 2013.Google Scholar
- M. Lewis, D. Yarats, Y. Dauphin, D. Parikh, and D. Batra. Deal or no deal? end-to-end learning of negotiation dialogues. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2433--2443, Copenhagen, Denmark, September 2017. Association for Computational Linguistics.Google ScholarCross Ref
- J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Confer- ence of the North American Chapter of the Associa- tion for Computational Linguistics: Human Language Technologies, pages 110--119, San Diego, California, June 2016. Association for Computational Linguistics.Google ScholarCross Ref
- J. Li, M. Galley, C. Brockett, G. Spithourakis, J. Gao, and B. Dolan. A persona-based neural conversation model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 994--1003, Berlin, Germany, August 2016. Association for Computational Linguistics.Google ScholarCross Ref
- J. Li, A. H. Miller, S. Chopra, M. Ranzato, and J. Weston. Learning through dialogue interactions by asking questions. arXiv preprint.Google Scholar
- J. Li, A. H. Miller, S. Chopra, M. Ranzato, and J. Weston. Dialogue learning with human-in-the-loop. arXiv preprint arXiv:1611.09823, 2016.Google Scholar
- J. Li, W. Monroe, and J. Dan. A simple, fast diverse decoding algorithm for neural generation. 2016.Google Scholar
- J. Li, W. Monroe, A. Ritter, D. Jurafsky, M. Galley, and J. Gao. Deep reinforcement learning for dialogue generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1192--1202, Austin, Texas, November 2016. Association for Computational Linguistics.Google ScholarCross Ref
- X. Li, Y.-N. Chen, L. Li, and J. Gao. End-to-end task completion neural dialogue systems. arXiv preprint arXiv:1703.01008, 2017.Google Scholar
- C.-W. Liu, R. Lowe, I. Serban, M. Noseworthy, L. Charlin, and J. Pineau. How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2122--2132, Austin, Texas, November 2016. Association for Computational Linguistics.Google ScholarCross Ref
- R. Lowe, N. Pow, I. Serban, and J. Pineau. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. In Proceedings of the 16th Annual Meeting of the Special In- terest Group on Discourse and Dialogue, pages 285--294, Prague, Czech Republic, September 2015. Association for Computational Linguistics.Google ScholarCross Ref
- Z. Lu and H. Li. A deep architecture for matching short texts. In International Conference on Neural Information Processing Systems, pages 1367--1375, 2013. Google ScholarDigital Library
- T. Luong, I. Sutskever, Q. Le, O. Vinyals, and W. Zaremba. Addressing the rare word problem in neural machine translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 11--19, Beijing, China, July 2015. Association for Computational Linguistics.Google ScholarCross Ref
- G. Mesnil, X. He, L. Deng, and Y. Bengio. Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding. Interspeech, 2013.Google Scholar
- T. Mikolov, M. Karaät, L. Burget, J. Cernocky, and S. Khudanpur. Recurrent neural network based language model. In Interspeech, volume 2, page 3, 2010.Google ScholarCross Ref
- T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111--3119, 2013. Google ScholarDigital Library
- A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J. Weston. Key-value memory networks for directly reading documents. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1400--1409, Austin, Texas, November 2016. Association for Computational Linguistics.Google ScholarCross Ref
- K. Mo, S. Li, Y. Zhang, J. Li, and Q. Yang. Personalizing a dialogue system with transfer reinforcement learning. 2016.Google Scholar
- S. Möller, R. Englert, K. Engelbrecht, V. Hafner, A. Jameson, A. Oulasvirta, A. Raake, and N. Reithinger. Memo: towards automatic usability evaluation of spoken dialogue services by user error simulations. In Ninth International Conference on Spoken Language Processing, 2006.Google Scholar
- L. Mou, Y. Song, R. Yan, G. Li, L. Zhang, and Z. Jin. Sequence to backward and forward sequences: A content-introducing approach to generative shorttext conversation. In 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.Google Scholar
- N. Mrkšić, D. Ó Séaghdha, B. Thomson, M. Gašić, P.-H. Su, D. Vandyke, T.-H. Wen, and S. Young. Multidomain dialog state tracking using recurrent neural networks. In 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), pages 794--799, Beijing, China, July 2015. Association for Computational Linguistics.Google Scholar
- N. Mrkšić, D. Ó Séaghdha, T.-H. Wen, B. Thomson, and S. Young. Neural belief tracker: Data-driven dialogue state tracking. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1777--1788, Vancouver, Canada, July 2017. Association for Computational Linguistics.Google ScholarCross Ref
- N. Papernot, M. Abadi, Ú. Erlingsson, I. Goodfellow, and K. Talwar. Semi-supervised knowledge transfer for deep learning from private training data. ICLR, 2017.Google Scholar
- Q. Qian, M. Huang, H. Zhao, J. Xu, and X. Zhu. Assigning personality/identity to a chatting machine for coherent conversation generation. 2017.Google Scholar
- M. Qiu, F.-L. Li, S. Wang, X. Gao, Y. Chen, W. Zhao, H. Chen, J. Huang, and W. Chu. Alime chat: A sequence to sequence and rerank based chatbot engine. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), volume 2, pages 498--503, 2017.Google ScholarCross Ref
- H. Ren, W. Xu, Y. Zhang, and Y. Yan. Dialog state tracking using conditional random fields. In SIGDIAL Conference, pages 457--461, 2013.Google Scholar
- A. Ritter, C. Cherry, and W. B. Dolan. Data-driven response generation in social media. In Conference on Empirical Methods in Natural Language Processing, pages 583--593, 2011. Google ScholarDigital Library
- G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5):513--523, 1988. Google ScholarDigital Library
- R. Sarikaya, G. E. Hinton, and B. Ramabhadran. Deep belief nets for natural language call-routing. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 5680--5683, 2011.Google ScholarCross Ref
- I. Serban, T. Klinger, G. Tesauro, K. Talamadupula, B. Zhou, Y. Bengio, and A. Courville. Multiresolution recurrent neural networks: An application to dialogue response generation. 2017.Google Scholar
- I. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. Building end-to-end dialogue systems using generative hierarchical neural network models, 2016.Google Scholar
- I. Serban, A. Sordoni, R. Lowe, L. Charlin, J. Pineau, A. Courville, and Y. Bengio. A hierarchical latent variable encoder-decoder model for generating dialogues. 2017.Google Scholar
- I. V. Serban, C. Sankar, M. Germain, S. Zhang, Z. Lin, S. Subramanian, T. Kim, M. Pieper, S. Chandar, N. R. Ke, et al. A deep reinforcement learning chatbot. arXiv preprint arXiv:1709.02349, 2017.Google Scholar
- L. Shang, Z. Lu, and H. Li. Neural responding machine for short-text conversation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1577--1586, Beijing, China, July 2015. Association for Computational Linguistics.Google ScholarCross Ref
- L. Shao, S. Gouws, D. Britz, A. Goldie, and B. Strope. Generating long and diverse responses with neural conversation models. 2017.Google Scholar
- X. Shen, H. Su, Y. Li, W. Li, S. Niu, Y. Zhao, A. Aizawa, and G. Long. A conditional variational framework for dialog generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 504--509, Vancouver, Canada, July 2017. Association for Computational Linguistics.Google ScholarCross Ref
- Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd International Conference on World Wide Web, pages 373--374. ACM, 2014. Google ScholarDigital Library
- K. Sohn, X. Yan, and H. Lee. Learning structured output representation using deep conditional generative models. In International Conference on Neural Information Processing Systems, pages 3483--3491, 2015. Google ScholarDigital Library
- Y. Song, R. Yan, X. Li, D. Zhao, and M. Zhang. Two are better than one: An ensemble of retrievaland generation-based dialog systems. arXiv preprint arXiv:1610.07149, 2016.Google Scholar
- A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J.-Y. Nie, J. Gao, and B. Dolan. A neural network approach to context-sensitive generation of conversational responses. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 196--205, Denver, Colorado, May-June 2015. Association for Computational Linguistics.Google ScholarCross Ref
- A. Stent, M. Marge, and M. Singhai. Evaluating evaluation methods for generation in the presence of variation. In International Conference on Computational Linguistics and Intelligent Text Processing, pages 341--351, 2005. Google ScholarDigital Library
- A. Stent, R. Prasad, and M. Walker. Trainable sentence planning for complex information presentation in spoken dialog systems. In Proceedings of the 42nd annual meeting on association for computational linguistics, page 79. Association for Computational Linguistics, 2004. Google ScholarDigital Library
- P.-H. Su, D. Vandyke, M. Gašić, D. Kim, N. Mrkšić, T.-H. Wen, and S. Young. Learning from real users: Rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems. arXiv preprint arXiv:1508.03386, 2015.Google Scholar
- Z. Tian, R. Yan, L. Mou, Y. Song, Y. Feng, and D. Zhao. How to make context more useful? an empirical study on context-aware neural conversational models. In Meeting of the Association for Computational Linguistics, pages 231--236, 2017.Google ScholarCross Ref
- V. K. Tran and L. M. Nguyen. Semantic refinement gru-based neural language generation for spoken dialogue systems. In PACLING, 2017.Google Scholar
- G. Tur, L. Deng, D. Hakkani-Tür, and X. He. Towards deeper understanding: Deep convex networks for semantic utterance classification. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pages 5045--5048. IEEE, 2012.Google ScholarCross Ref
- O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015.Google Scholar
- P. Vougiouklis, J. Hare, and E. Simperl. A neural network approach for knowledge-driven response generation. In 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.Google Scholar
- M. A. Walker, D. J. Litman, C. A. Kamm, and A. Abella. Paradise: A framework for evaluating spoken dialogue agents. In Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, pages 271--280. Association for Computational Linguistics, 1997. Google ScholarDigital Library
- M. A. Walker, O. C. Rambow, and M. Rogati. Training a sentence planner for spoken dialogue using boosting. Computer Speech & Language, 16(3):409--433, 2002.Google ScholarCross Ref
- H. Wang, Z. Lu, H. Li, and E. Chen. A dataset for research on short-text conversations. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 935--945, Seattle, Washington, USA, October 2013. Association for Computational Linguistics.Google Scholar
- M. Wang, Z. Lu, H. Li, and Q. Liu. Syntax-based deep matching of short texts. 03 2015.Google Scholar
- Z. Wang and O. Lemon. A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information. In SIGDIAL Conference, pages 423--432, 2013.Google Scholar
- T.-H. Wen, M. Gašić, D. Kim, N. Mrkšić, P.-H. Su, D. Vandyke, and S. Young. Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 275--284, Prague, Czech Republic, September 2015. Association for Computational Linguistics.Google ScholarCross Ref
- T.-H. Wen, M. Gašić, N. Mrkšić, P.-H. Su, D. Vandyke, and S. Young. Semantically conditioned lstm-based natural language generation for spoken dialogue systems. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1711--1721, Lisbon, Portugal, September 2015. Association for Computational Linguistics.Google ScholarCross Ref
- T.-H. Wen, M. Gašić, N. Mrkšić, L. M. Rojas-Barahona, P.-H. Su, D. Vandyke, and S. Young. Multidomain neural network language generation for spoken dialogue systems. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 120--129, San Diego, California, June 2016. Association for Computational Linguistics.Google Scholar
- T.-H. Wen, D. Vandyke, N. Mrkšić, M. Gasic, L. M. Rojas Barahona, P.-H. Su, S. Ultes, and S. Young. A network-based end-to-end trainable task-oriented dialogue system. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 438--449, Valencia, Spain, April 2017. Association for Computational Linguistics.Google ScholarCross Ref
- J. Williams. Multi-domain learning and generalization in dialog state tracking. In SIGDIAL Conference, pages 433--441, 2013.Google Scholar
- J. Williams, A. Raux, D. Ramachandran, and A. Black. The dialog state tracking challenge. In Proceedings of the SIGDIAL 2013 Conference, pages 404--413, 2013.Google Scholar
- J. D. Williams. A belief tracking challenge task for spoken dialog systems. In NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data, pages 23--24, 2012. Google ScholarDigital Library
- J. D. Williams. Web-style ranking and slu combination for dialog state tracking. In SIGDIAL Conference, pages 282--291, 2014.Google ScholarCross Ref
- J. D. Williams, K. Asadi, and G. Zweig. Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 665--677, Vancouver, Canada, July 2017. Association for Computational Linguistics.Google ScholarCross Ref
- J. D. Williams and G. Zweig. End-to-end lstm-based dialog control optimized with supervised and reinforcement learning. arXiv preprint arXiv:1606.01269, 2016.Google Scholar
- R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3-4):229--256, 1992. Google ScholarDigital Library
- Y. Wu, W. Wu, Z. Li, and M. Zhou. Topic augmented neural network for short text conversation. 2016.Google Scholar
- Y. Wu, W. Wu, C. Xing, M. Zhou, and Z. Li. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 496--505, Vancouver, Canada, July 2017. Association for Computational Linguistics.Google ScholarCross Ref
- C. Xing, W. Wu, Y. Wu, J. Liu, Y. Huang, M. Zhou, and W. Y. Ma. Topic augmented neural response generation with a joint attention mechanism. 2016.Google Scholar
- C. Xing, W. Wu, Y. Wu, J. Liu, Y. Huang, M. Zhou, and W.-Y. Ma. Topic aware neural response generation. 2017.Google Scholar
- C. Xing, W. Wu, Y. Wu, M. Zhou, Y. Huang, and W. Y. Ma. Hierarchical recurrent attention network for response generation. 2017.Google Scholar
- R. Yan, Y. Song, and H. Wu. Learning to respond with deep neural networks for retrieval-based humancomputer conversation system. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'16, pages 55--64, New York, NY, USA, 2016. ACM. Google ScholarDigital Library
- Z. Yan, N. Duan, P. Chen, M. Zhou, J. Zhou, and Z. Li. Building task-oriented dialogue systems for online shopping, 2017.Google Scholar
- D. Yann, G. Tur, D. Hakkani-Tur, and L. Heck. Zeroshot learning and clustering for semantic utterance classification using deep learning, 2014.Google Scholar
- K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, and Y. Shi. Spoken language understanding using long short-term memory neural networks. pages 189--194, 2014.Google Scholar
- K. Yao, B. Peng, G. Zweig, and K. F.Wong. An attentional neural conversation model with improved specicity. 2016.Google Scholar
- K. Yao, G. Zweig, M. Y. Hwang, Y. Shi, and D. Yu. Recurrent neural networks for language understanding. In Interspeech, 2013.Google Scholar
- J. Yin, X. Jiang, Z. Lu, L. Shang, H. Li, and X. Li. Neural generative question answering. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16, pages 2972--2978. AAAI Press, 2016. Google ScholarDigital Library
- S. Young, M. Gai, S. Keizer, F. Mairesse, J. Schatzmann, B. Thomson, and K. Yu. The hidden information state model: A practical framework for pomdpbased spoken dialogue management. 24(2):150--174, 2010. Google ScholarDigital Library
- R. Zens, F. J. Och, and H. Ney. Phrase-based statistical machine translation. In German Conference on Ai: Advances in Artificial Intelligence, pages 18--32, 2002. Google ScholarDigital Library
- W. Zhang, T. Liu, Y. Wang, and Q. Zhu. Neural personalized response generation as domain adaptation. 2017.Google Scholar
- T. Zhao and M. Eskenazi. Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 1--10, Los Angeles, September 2016. Association for Computational Linguistics.Google ScholarCross Ref
- W. X. Zhao, J. Jiang, J. Weng, J. He, E. P. Lim, H. Yan, and X. Li. Comparing twitter and traditional media using topic models. In European Conference on Advances in Information Retrieval, pages 338--349, 2011. Google ScholarDigital Library
- H. Zhou, M. Huang, T. Zhang, X. Zhu, and B. Liu. Emotional chatting machine: Emotional conversation generation with internal and external memory. 2017.Google Scholar
- H. Zhou, M. Huang, and X. Zhu. Context-aware natural language generation for spoken dialogue systems. In COLING, pages 2032--2041, 2016.Google Scholar
- X. Zhou, D. Dong, H. Wu, S. Zhao, D. Yu, H. Tian, X. Liu, and R. Yan. Multi-view response selection for human-computer conversation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 372--381, 2016.Google ScholarCross Ref
Index Terms
- A Survey on Dialogue Systems: Recent Advances and New Frontiers
Recommendations
Towards Conversationally Intelligent Dialog Systems
CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing SystemsSpoken dialog systems, lacking the means to address the complex phenomena of spontaneous speech and conversational dynamics, force users into a constrained mode of dialog that resembles text-based interaction more closely than spoken conversation. Turn-...
Annotating dialogue acts to construct dialogue systems for consulting
ALR7: Proceedings of the 7th Workshop on Asian Language ResourcesThis paper introduces a new corpus of consulting dialogues, which is designed for training a dialogue manager that can handle consulting dialogues through spontaneous interactions from the tagged dialogue corpus. We have collected 130 h of consulting ...
Exploring Mixed-Initiative Dialogue Using Computer Dialogue Simulation
This paper experimentally shows that mixed-initiative dialogue is not always more efficient than non-mixed initiative dialogue in route finding tasks. Based on the dialogue model proposed in Conversation Analysis and Discourse Analysis a lá the ...
Comments