Skip to main content
SpringerLink
Log in

Learning to Ask Specific Questions Naturally in Chat-Oriented Dialogue Systems

  • Conference paper
  • First Online:
Conversational AI for Natural Human-Centric Interaction

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 943))

  • 445 Accesses

Abstract

In order for a dialogue system to provide information tailored to the user, it is important to ask users questions and obtain the necessary information. However, asking questions abruptly or in a self-centered manner would be undesirable because it may disrupt the flow of conversation and decrease the user’s satisfaction. In this work, we propose a response generation model for a chat-oriented dialogue system that can ask specific questions naturally. Specifically, we train a response generation model that generates utterances on the basis of both the dialogue context and the question to be asked. The results of simulations and human evaluations demonstrate that the proposed model can make it easier for a system to ask specific questions while maintaining the naturalness of the dialogue.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://parl.ai/projects/recipes/.

  2. 2.

    https://parl.ai/.

References

  1. Adiwardana D, Luong M, So DR, Hall J, Fiedel N, Thoppilan R, Yang Z, Kulshreshtha A, Nemade G, Lu Y, Le QV (2020) Towards a human-like open-domain chatbot. arXiv:2001.09977

  2. Budzianowski P, Vulić I (2019) Hello, it’s GPT-2 - how can I help you? Towards the use of pretrained language models for task-oriented dialogue systems. In: Proceedings of the 3rd workshop on neural generation and translation, pp 15–22

    Google Scholar 

  3. Cho K, van Merriënboer B, Gulcehre C, 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, pp 1724–1734

    Google Scholar 

  4. Dinan E, Roller S, Shuster K, Fan A, Auli M, Weston J (2019) Wizard of Wikipedia: Knowledge-powered conversational agents. In: Proceedings of the 7th international conference on learning representations

    Google Scholar 

  5. Ham D, Lee JG, Jang Y, Kim KE (2020) End-to-end neural pipeline for goal-oriented dialogue systems using GPT-2. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 583–592

    Google Scholar 

  6. Hoque ME, Courgeon M, Martin JC, Mutlu B, Picard RW (2013) Mach: my automated conversation coach. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp 697–706

    Google Scholar 

  7. Inoue K, Hara K, Lala D, Nakamura S, Takanashi K, Kawahara T (2019) A job interview dialogue system with autonomous android ERICA. In: Marchi E, Siniscalchi SM, Cumani S, Salerno VM, Li H (eds) Proceedings of the 10th international workshop on spoken dialogue systems, vol 714, pp 291–297

    Google Scholar 

  8. Johnson M, Schuster M, Le QV, Krikun M, Wu Y, Chen Z, Thorat N, Viégas F, Wattenberg M, Corrado G, Hughes M, Dean J (2017) Google’s multilingual neural machine translation system: enabling zero-shot translation. Trans Assoc Comput Linguist 5:339–351

    Article  Google Scholar 

  9. Johnston M, Ehlen P, Conrad FG, Schober MF, Antoun C, Fail S, Hupp A, Vickers L, Yan H, Zhang C (2013) Spoken dialog systems for automated survey interviewing. In: Proceedings of the 2013 annual meeting of the special interest group on discourse and dialogue, pp 329–333

    Google Scholar 

  10. Kahn G, Nowlan S, Mcdermott J (1985) More: an intelligent knowledge acquisition tool. In: Proceedings of the 9th international joint conference on artificial intelligence, pp 581–584

    Google Scholar 

  11. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

  12. Kishinami Y, Akama R, Sato S, Suzuki J, Tokuhisa R, Inui K (2021) Data-oriented approach for lookahead response generation. In Proceedings of the 2021 annual conference of JSAI, p 3J2GS6b02. (in Japanese)

    Google Scholar 

  13. Kobori T, Nakano M, Nakamura T (2016) Small talk improves user impressions of interview dialogue systems. In: Proceedings of the 17th annual meeting of the special interest group on discourse and dialogue, pp 370–380

    Google Scholar 

  14. Kulikov I, Lee J, Cho K (2019) Multi-turn beam search for neural dialogue modeling. arXiv:1906.00141

  15. Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7871–7880

    Google Scholar 

  16. Li J, Monroe W, Ritter A, Jurafsky D, Galley M, Gao J (2016) Deep reinforcement learning for dialogue generation. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 1192–1202

    Google Scholar 

  17. Paulus R, Xiong C, Socher R (2017) A deep reinforced model for abstractive summarization. arXiv:1705.04304

  18. 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):1–67

    MathSciNet  MATH  Google Scholar 

  19. Rashkin H, Smith EM, Li M, Boureau YL (2019) Towards empathetic open-domain conversation models: a new benchmark and dataset. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5370–5381

    Google Scholar 

  20. Roller S, Dinan E, Goyal N, Ju D, Williamson M, Liu Y, Xu J, Ott M, Shuster K, Smith EM, Boureau Y, Weston J (2020) Recipes for building an open-domain chatbot. arXiv:2004.13637

  21. Sennrich R, Haddow B, Birch A (2016) Neural machine translation of rare words with subword units. In: Proceedings of the 54th annual meeting of the association for computational linguistics, vol 1, pp 1715–1725

    Google Scholar 

  22. Smith EM, Williamson M, Shuster K, Weston J, Boureau YL (2020) Can you put it all together: evaluating conversational agents’ ability to blend skills. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 2021–2030

    Google Scholar 

  23. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th neural information processing systems, pp 3104–3112

    Google Scholar 

  24. Talmor A, Herzig J, Lourie N, Berant J (2019) CommonsenseQA: a question answering challenge targeting commonsense knowledge. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 4149–4158

    Google Scholar 

  25. Tang J, Zhao T, Xiong C, Liang X, Xing E, Hu Z (2019) Target-guided open-domain conversation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5624–5634

    Google Scholar 

  26. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Lu, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 2017 advances in neural information processing systems, vol 30

    Google Scholar 

  27. Wen TH, Vandyke D, Mrkšić N, Gašić M, Rojas-Barahona LM, Su PH, Ultes S, Young S (2017) 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, vol 1, pp 438–449

    Google Scholar 

  28. Xing C, Wu W, Wu Y, Liu J, Huang Y, Zhou M, Ma WY (2017) Topic aware neural response generation. In: Proceedings of the 31th AAAI conference on artificial intelligence, pp 3351–3357

    Google Scholar 

  29. Xu J, Wang H, Niu Z, Wu H, Che W (2020) Knowledge graph grounded goal planning for open-domain conversation generation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 9338–9345

    Google Scholar 

  30. Yang Y, Li Y, Quan X (2020) UBAR: towards fully end-to-end task-oriented dialog systems with GPT-2. arXiv:2012.03539

  31. 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, vol 1, pp 2204–2213

    Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers for their helpful comments and suggestions. Funding was provided by a Grant-in-Aid for Scientific Research (Grant no. JP19H01125).

Author information

Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Sota Horiuchi & Ryuichiro Higashinaka

Authors
  1. Sota Horiuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryuichiro Higashinaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sota Horiuchi .

Editor information

Editors and Affiliations

  1. Toshiba (United Kingdom), Weybridge, UK

    Svetlana Stoyanchev

  2. Daimler (Germany), Stuttgart, Germany

    Stefan Ultes

  3. The Chinese University of Hong Kong, Shenzhen, China

    Haizhou Li

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Horiuchi, S., Higashinaka, R. (2022). Learning to Ask Specific Questions Naturally in Chat-Oriented Dialogue Systems. In: Stoyanchev, S., Ultes, S., Li, H. (eds) Conversational AI for Natural Human-Centric Interaction. Lecture Notes in Electrical Engineering, vol 943. Springer, Singapore. https://doi.org/10.1007/978-981-19-5538-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5538-9_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5537-2

  • Online ISBN: 978-981-19-5538-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation