Skip to main content
SpringerLink
Log in

Sequential or jumping: context-adaptive response generation for open-domain dialogue systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Neural response generation can automatically produce replies for open-domain dialogue systems without hand-crafted rules or templates. Current studies follow a non-context-adaptive paradigm that employs a single response generator to deal with all dialogues. However, as a dialogue progresses, its textual characteristics (e.g., context length, information volume, involving topics) are changing, so are the issues challenging its response generation. Non-context-adaptive response generators are inflexible and may fail to achieve globally good performance without considering the differences existing among dialogues. In this paper, we propose a novel framework named as C ontext-A daptive R esponse G eneration (CARG), which assembles two different response generators to respectively handle long and short dialogues. Specifically, given a dialogue, CARG first classifies it into short or long types according to the number of its containing utterances. For a short dialogue, CARG employs a sequential reader (SR) to concatenates all utterances into a sequence aiming to construct the dialogue context by limited semantics. For a long dialogue where irrelevant noises and relevant contexts both exist, CARG uses a jumping reader (JR) to generate the response, which treats the latest utterance as the anchor and further performs selective context utilization under its guidance. We introduce ensemble learning strategy to conduct the training and testing of CARG. Extensive experimental results on two benchmark chat corpora show that the proposed CARG framework can outperform various competitive baselines, validating its effectiveness on response generation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. The dataset can be downloaded at http://yanran.li/dailydialog.html.

  2. The dataset can be downloaded at https://github.com/facebookresearch/ParlAIhttps://github.com/facebookresearch/ParlAI.

  3. The code of our own implementations will be open-sourced at https://github.com/katherinelyx when published.

  4. https://simpletransformers.ai/

References

  1. Brown T.B, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D (2020) Language Models are Few-Shot Learners. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

  2. Chen H, Ren Z, Tang J, Zhao Y.E, Yin D (2018) Hierarchical variational memory network for dialogue generation. In: Proceedings of the 2018 world wide web conference on world wide web, WWW 2018, Lyon, France, April 23-27, 2018, pp 1653–1662. https://doi.org/10.1145/3178876.3186077

  3. Cho K, van Merrienboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. In: Proceedings of SSST@EMNLP 2014, eighth workshop on syntax, semantics and structure in statistical translation, Doha, Qatar, 25 October 2014, pp 103–111. Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-4012, https://aclanthology.org/W14-4012/

  4. Cho K, van Merrienboer B, Gülçehre Ç, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) 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 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, ACL, pp 1724–1734. https://doi.org/10.3115/v1/d14-1179

  5. Clark L, Pantidi N, Cooney O, Doyle PR, Garaialde D, Edwards J, Spillane B, Gilmartin E, Murad C, Munteanu C, Wade V, Cowan BR (2019) What makes a good conversation?: challenges in designing truly conversational agents. In: Proceedings of the 2019 CHI conference on human factors in computing systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, ACM p 475. https://doi.org/10.1145/3290605.3300705

  6. Cuayáhuitl H, Lee D, Ryu S, Cho Y, Choi S, Indurthi SR, Yu S, Choi H, Hwang I, Kim J (2019) Ensemble-based deep reinforcement learning for chatbots. Neurocomputing 366:118–130. https://doi.org/10.1016/j.neucom.2019.08.007

    Article  Google Scholar 

  7. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp 4171–4186. Association for Computational Linguistics. https://doi.org/10.18653/v1/n19-1423

  8. Gao J, Galley M, Li L (2019) Neural approaches to conversational AI. Found Trends Inf Retr 13(2-3):127–298. https://doi.org/10.1561/1500000074

    Article  Google Scholar 

  9. Gu JC, Ling ZH, Zhu X, Liu Q (2019) Dually interactive matching network for personalized response selection in retrieval-Based chatbots. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, Association for Computational Linguistics, pp 1845–1854

  10. Hao M, Cao W, Liu Z, Wu M, Xiao P (2020) Visual-audio emotion recognition based on multi-task and ensemble learning with multiple features. Neurocomputing 391:42–51. https://doi.org/10.1016/j.neucom.2020.01.048

    Article  Google Scholar 

  11. Huang M, Zhu X, Gao J (2020) Challenges in building intelligent open-domain dialog systems. ACM Trans Inf Syst 38(3):21:1–21:32. https://doi.org/10.1145/3383123

    Article  Google Scholar 

  12. Lan T, Mao X, Wei W, Gao X, Huang H (2020) PONE: a novel automatic evaluation metric for open-domain generative dialogue systems. ACM Trans Inf Syst 39(1):71–737. https://doi.org/10.1145/3423168

    Google Scholar 

  13. Li J, Galley M, Brockett C, Gao J, Dolan B (2016) A diversity-promoting objective function for neural conversation models. In: NAACL HLT 2016, The 2016 conference of the north American chapter of the association for computational linguistics: human language technologies, San Diego California, USA, June 12-17, 2016, The Association for Computational Linguistics, pp 110–119. https://doi.org/10.18653/v1/n16-1014

  14. Li X, Xiong H, Chen Z, Huan J, Xu C, Dou D (2021) In-network ensemble: deep ensemble learning with diversified knowledge distillation. ACM Trans Intell Syst Technol 12(5):63:1–63:19. https://doi.org/10.1145/3473464

    Article  Google Scholar 

  15. Li Y, Su H, Shen X, Li W, Cao Z, Niu S (2017) Dailydialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of the eighth international joint conference on natural language processing, IJCNLP 2017, Taipei, Taiwan, November 27 - December 1, 2017 - Volume 1: Long Papers, Asian Federation of Natural Language Processing, pp 986–995

  16. Lin Z, Cai D, Wang Y, Liu X, Zheng H, Shi S (2020) The world is not binary: learning to rank with grayscale data for dialogue response selection. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), Association for Computational Linguistics, pp 9220–9229. https://doi.org/10.18653/v1/2020.emnlp-main.741. https://www.aclweb.org/anthology/2020.emnlp-main.741

  17. Ling Y, Cai F, Chen H, de Rijke M (2020) Leveraging context for neural question generation in open-domain dialogue systems. In: WWW ’20: the web conference 2020, Taipei, Taiwan, April 20-24, 2020, ACM / IW3C2, pp 2486–2492. https://doi.org/10.1145/3366423.3379996

  18. Ling Y, Cai F, Hu X, Liu J, Chen W, Chen H (2021) Context-controlled topic-aware neural response generation for open-domain dialog systems. Inf Process Manag 58(1):102392. https://doi.org/10.1016/j.ipm.2020.102392

    Article  Google Scholar 

  19. Liu Y, Feng S, Wang D, Song K, Ren F, Zhang Y (2021) A graph reasoning network for multi-turn response selection via customized pre-training. In: Thirty-Fifth AAAI conference on artificial intelligence, AAAI 2021, thirty-third conference on innovative applications of artificial intelligence, IAAI 2021, The eleventh symposium on educational advances in artificial intelligence, EAAI 2021, virtual event, February 2-9, 2021, AAAI Press, pp 13433–13442. https://ojs.aaai.org/index.php/AAAI/article/view/17585

  20. Ma W, Cui Y, Liu T, Wang D, Wang S, Hu G (2020) Conversational word embedding for retrieval-based dialog system. In: Proceedings of the 58th annual meeting of the association for computational linguistics pp 1375–1380. ArXiv:2004.13249, https://doi.org/10.18653/v1/2020.acl-main.127

  21. Mao Y, Cai F, Guo Y, Chen H (2021) Incorporating emotion for response generation in multi-turn dialogues. Appl Intell. https://doi.org/10.1007/s10489-021-02819-z

  22. Olabiyi O, Khazane A, Salimov A, Mueller E (2019) An adversarial learning framework for a persona-based multi-turn dialogue model. In: Proceedings of the workshop on methods for optimizing and evaluating neural language generation. https://doi.org/10.18653/v1/W19-2301. http://aclweb.org/anthology/W19-2301. Association for Computational Linguistics, Minneapolis, Minnesota, pp 1–10

  23. Pan Z.F, Bai K, Wang Y, Zhou L, Liu X (2019) Improving open-domain dialogue systems via multi-turn incomplete utterance restoration. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, Association for Computational Linguistics, pp 1824–1833. https://doi.org/10.18653/v1/D19-1191

  24. Papineni K, Roukos S, Ward T, Zhu W (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, July 6-12, 2002, Philadelphia, PA, USA, ACL, pp. 311–318. https://doi.org/10.3115/1073083.1073135. https://aclanthology.org/P02-1040/

  25. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21:140:1–140:67

    MathSciNet  MATH  Google Scholar 

  26. Serban IV, Sordoni A, Bengio Y, Courville AC, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, AAAI Press, pp 3776–3784

  27. Serban IV, Sordoni A, Lowe R, Charlin L, Pineau J, Courville AC, Bengio Y (2017) A hierarchical latent variable encoder-decoder model for generating dialogues. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, February 4-9, 2017, San Francisco, California, USA, AAAI Press, pp 3295–3301

  28. Shen L, Feng Y (2020) CDL: curriculum dual learning for emotion-controllable response generation. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5-10, 2020, Association for Computational Linguistics, pp 556–566. https://doi.org/10.18653/v1/2020.acl-main.52https://doi.org/10.18653/v1/2020.acl-main.52

  29. Shen X, Su H, Niu S, Demberg V (2018) Improving variational encoder-decoders in dialogue generation. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, AAAI Press, pp 5456–5463. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16402

  30. Shum H, He X, Li D (2018) From eliza to xiaoice: challenges and opportunities with social chatbots. Frontiers Inf Technol Electron Eng 19(1):10–26. https://doi.org/10.1631/FITEE.1700826

    Article  Google Scholar 

  31. Song Y, Li CT, Nie JY, Zhang M, Zhao D, Yan R (2018) An ensemble of retrieval-based and generation-based human-computer conversation systems. In: Proceedings of the 27th international joint conference on artificial intelligence, IJCAI’18, AAAI Press, p 4382–4388

  32. Su H, Shen X, Zhang R, Sun F, Hu P, Niu C, Zhou J (2019) Improving multi-turn dialogue modelling with utterance rewriter. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Association for Computational Linguistics, pp 22–31. https://doi.org/10.18653/v1/p19-1003

  33. Su Y, Cai D, Zhou Q, Lin Z, Baker S, Cao Y, Shi S, Collier N, Wang Y (2021) Dialogue response selection with hierarchical curriculum learning. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Association for Computational Linguistics, pp 1740–1751. https://doi.org/10.18653/v1/2021.acl-long.137

  34. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems 27: annual conference on neural information processing systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp 3104–3112

  35. Tuan Y, Chen Y, Lee H (2019) Dykgchat: Benchmarking dialogue generation grounding on dynamic knowledge graphs. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, Association for Computational Linguistics, pp 1855–1865. https://doi.org/10.18653/v1/D19-1194

  36. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 5998–6008

  37. Wang W, Huang M, Xu X, Shen F, Nie L (2018) Chat more: deepening and widening the chatting topic via a deep model. In: The 41st International ACM SIGIR conference on research & development in information retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018, ACM, pp 255–264. https://doi.org/10.1145/3209978.3210061

  38. Wang Y, Wu L, Xia Y, Qin T, Zhai C, Liu T (2020) Transductive ensemble learning for neural machine translation. In: The thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, the thirty-second innovative applications of artificial intelligence conference, IAAI 2020, The tenth AAAI symposium on educational advances in artificial intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, AAAI Press, pp 6291–6298. https://ojs.aaai.org/index.php/AAAI/article/view/6097

  39. Williams JD, Raux A, Henderson M (2016) The dialog state tracking challenge series: a review. Dialogue Discourse 7(3):4–33. http://dad.uni-bielefeld.de/index.php/dad/article/view/3685

    Article  Google Scholar 

  40. Wu Q, Wang P, Shen C, Reid ID, van den Hengel A (2018) Are you talking to me? reasoned visual dialog generation through adversarial learning. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, Computer Vision Foundation / IEEE Computer Society, pp 6106–6115. https://doi.org/10.1109/CVPR.2018.00639. http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Are_You_Talking_CVPR_2018_paper.html

  41. Wu Y, Wei F, Huang S, Wang Y, Li Z, Zhou M (2019) Response generation by context-aware prototype editing. In: The thirty-third AAAI conference on artificial intelligence, AAAI 2019, the thirty-first innovative applications of artificial intelligence conference, IAAI 2019, the ninth AAAI symposium on educational advances in artificial intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, AAAI Press, pp 7281–7288. https://doi.org/10.1609/aaai.v33i01.33017281

  42. Xing C, Wu Y, Wu W, Huang Y, Zhou M (2018) Hierarchical recurrent attention network for response generation. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, AAAI Press, pp 5610–5617

  43. Xu F, Xu G, Wang Y, Wang R, Ding Q, Liu P, Zhu Z (2021) Diverse dialogue generation by fusing mutual persona-aware and self-transferrer. https://doi.org/10.1007/s10489-021-02660-4

  44. Yang M, Huang W, Tu W, Qu Q, Shen Y, Lei K (2020) Multitask learning and reinforcement learning for personalized dialog generation: an empirical study. IEEE Transactions on Neural Networks and Learning Systems

  45. Zhang H, Liu Z, Xiong C, Liu Z (2020) Grounded conversation generation as guided traverses in commonsense knowledge graphs. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5-10, 2020, Association for Computational Linguistics, pp 2031–2043. https://doi.org/10.18653/v1/2020.acl-main.184

  46. Zhang J, Tao C, Xu Z, Xie Q, Chen W, Yan R (2019) Ensemblegan: Adversarial learning for retrieval-generation ensemble model on short-text conversation. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, SIGIR’19. https://doi.org/10.1145/3331184.3331193. Association for Computing Machinery, New York, pp 435–444

  47. Zhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J (2018) Personalizing dialogue agents: I have a dog, do you have pets too?. In: Proceedings of the 56th annual meeting of the association for computational linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, Association for Computational Linguistics, pp 2204–2213. https://doi.org/10.18653/v1/P18-1205. https://aclanthology.org/P18-1205/

  48. Zhang T, Kishore V, Wu F, Weinberger KQ, Artzi Y (2020) Bertscore: evaluating text generation with BERT. In: 8Th international conference on learning representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. Openreview.net

  49. Zhang W, Cui Y, Wang Y, Zhu Q, Li L, Zhou L, Liu T (2018) Context-sensitive generation of open-domain conversational responses. In: Proceedings of the 27th international conference on computational linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, Association for Computational Linguistics, pp 2437–2447. https://aclanthology.org/C18-1206/

  50. Zhang Y, Sun S, Galley M, Chen Y, Brockett C, Gao X, Gao J, Liu J, Dolan B (2020) DIALOGPT : large-scale generative pre-training for conversational response generation. In: Proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations, ACL 2020, Online, July 5-10, 2020, Association for Computational Linguistics, pp 270–278. https://doi.org/10.18653/v1/2020.acl-demos.30

  51. Zhang Y, Wang Y, Zhang L, Zhang Z, Gai K (2019) Improve diverse text generation by self labeling conditional variational auto encoder. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2019, Brighton, United Kingdom, May 12-17, 2019, IEEE, pp 2767–2771. https://doi.org/10.1109/ICASSP.2019.8683090

  52. Zhao X, Wu W, Xu C, Tao C, Zhao D, Yan R (2020) Knowledge-grounded dialogue generation with pre-trained language models. In: Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics, pp 3377–3390. https://doi.org/10.18653/v1/2020.emnlp-main.272

  53. Zhou K, Zhang K, Wu Y, Liu S, Yu J (2019) Unsupervised context rewriting for open domain conversation. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, Association for Computational Linguistics, pp 1834–1844. https://doi.org/10.18653/v1/D19-1192

  54. Zhou L, Gao J, Li D, Shum H (2020) The design and implementation of xiaoice, an empathetic social chatbot. Comput Linguist 46(1):53–93. https://doi.org/10.1162/coli_a_00368

    Article  Google Scholar 

  55. Zhou X, He J, Yang C (2022) An ensemble learning method based on deep neural network and group decision making. Knowl Based Syst 239:107801. https://doi.org/10.1016/j.knosys.2021.107801

    Article  Google Scholar 

  56. Zhou ZH (2012) Ensemble methods: foundations and algorithms ensemble methods: foundations and algorithms

Download references

Author information

Authors and Affiliations

  1. College of Information and Communication, National University of Defense Technology, Wuhan, Hubei, China

    Yanxiang Ling, Zheng Liang & Tianqi Wang

  2. Rocket Force University of Engineering, Xi’an, Shaanxi, China

    Zheng Liang

  3. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan, China

    Fei Cai & Honghui Chen

Authors
  1. Yanxiang Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Honghui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanxiang Ling.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ling, Y., Liang, Z., Wang, T. et al. Sequential or jumping: context-adaptive response generation for open-domain dialogue systems. Appl Intell 53, 11251–11266 (2023). https://doi.org/10.1007/s10489-022-04067-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04067-1

Keywords

Search

Navigation