Skip to main content

Eliciting Cooperative Persuasive Dialogue by Multimodal Emotional Robot

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

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

Abstract

Using emotional expressions is an effective dialogue technique in human–human dialogue. Introducing such techniques to human–robot interaction might improve their effectiveness to encourage the cooperative dialogue manner of system users. However, most of the existing research on emotional agent systems was based on the Wizard-of-Oz (WOZ) method to verify the abilities of interactive interfaces. In this paper, we build an autonomous dialogue robot that uses emotional expressions for eliciting the cooperative dialogue manner of users. The robot uses both verbal and multimodal expressions as well as emotional speech and emotional gestures in interactions. Our dialogue experiments showed that positive emotional expressions are the most efficient strategy for facilitating cooperative dialogues with users. Moreover, using negative emotional expressions is also an effective strategy in some dialogue contexts. We also investigated several modalities to emphasize the robot’s emotional expression abilities.

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

Access this chapter

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

References

  1. Adler RF, Iacobelli F, Gutstein Y (2016) Are you convinced? A wizard of OZ study to test emotional vs. rational persuasion strategies in dialogues. Comput Hum Behav 57:75–81

    Google Scholar 

  2. Asai S, Yoshino K, Shinagawa S, Sakti S, Nakamura S (2020) Emotional speech corpus for persuasive dialogue system. In: Proceedings of The 12th language resources and evaluation conference, pp 491–497. Marseille, France

    Google Scholar 

  3. Becker C, Kopp S, Wachsmuth I (2004) Simulating the emotion dynamics of a multimodal conversational agent. In: Tutorial and research workshop on affective dialogue systems. Springer, pp 154–165

    Google Scholar 

  4. Colombo P, Witon W, Modi A, Kennedy J, Kapadia M (2019) Affect-driven dialog generation. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 3734–3743

    Google Scholar 

  5. bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp 4171–4186 (2019)

    Google Scholar 

  6. Fogg B (1997) Captology: the study of computers as persuasive technologies. In: Proceedings CHI extended abstracts on HFCS, CHI EA ’97, p 129

    Google Scholar 

  7. Forgas JP (1998) On feeling good and getting your way: mood effects on negotiator cognition and bargaining strategies

    Google Scholar 

  8. Galley M, Brockett C, Gao X, Dolan B, Gao J (2019) End-to-end conversation modeling: moving beyond chitchat. In: AAAI the seventh dialogue system technology challenge

    Google Scholar 

  9. Ghosh S, Chollet M, Laksana E, Morency LP, Scherer S (2017) Affect-lm: a neural language model for customizable affective text generation. In: ACL

    Google Scholar 

  10. Goswamy T, Singh I, Barkati A, Modi A (2020) Adapting a language model for controlled affective text generation. In: Proceedings of the 28th international conference on computational linguistics, pp 2787–2801

    Google Scholar 

  11. Gunasekara C, Kummerfeld JK, Polymenakos L, Lasecki W (2019) Dstc7 task 1: noetic end-to-end response selection. In: Proceedings the first workshop on NLP for conversational AI, pp 60–67

    Google Scholar 

  12. Harris ZS (1954) Distributional structure. Word 10(2–3):146–162

    Article  Google Scholar 

  13. Heath R, Brandt D, Nairn A (2006) Brand relationships: strengthened by emotion, weakened by attention. J Advert Res 46(4):410–419

    Article  Google Scholar 

  14. Hiraoka T, Neubig G, Sakti S, Toda T, Nakamura S (2016) Learning cooperative persuasive dialogue policies using framing. Speech Commun 84:83–96

    Article  Google Scholar 

  15. Lhommet M, Marsella S (2014) Expressing emotion through posture. The Oxford handbook of affective computing, pp 273–285

    Google Scholar 

  16. Lorenzo-Trueba J, Barra-Chicote R, San-Segundo R, Ferreiros J, Yamagishi J, Montero J (2015) Emotion transplantation through adaptation in hmm-based speech synthesis. Comput Speech Lang 34(1):292–307. https://doi.org/10.1016/j.csl.2015.03.008

    Article  Google Scholar 

  17. Lubis N, Sakti S, Yoshino K, Nakamura S (2018) Eliciting positive emotion through affect-sensitive dialogue response generation: a neural network approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

    Google Scholar 

  18. Mehrabian A, Russell JA (1974) The basic emotional impact of environments. Percept Motor Skills 38(1):283–301

    Article  Google Scholar 

  19. Mizukami M, Kizuki H, Nomura T, Neubig G, Yoshino K, Sakti S, Toda T, Nakamura S (2015) Adaptive selection from multiple response candidates in example-based dialogue. In: 2015 IEEE workshop on automatic speech recognition and understanding, pp 784–790

    Google Scholar 

  20. Morris M, Keltner D (2000) How emotions work: the social functions of emotional expression in negotiations. Res Organ Behav 22:1–50

    Google Scholar 

  21. Sakaki T, Mizuki S, Gunji N (2019) Bert pre-trained model trained on large-scale Japanese social media corpus. Hottolink

    Google Scholar 

  22. Santhanam S, Shaikh S (2019) Emotional neural language generation grounded in situational contexts. In: Proceedings the 4th workshop on computational creativity in language generation, pp 22–27

    Google Scholar 

  23. Serban IV, Sordoni A, Lowe R, Charlin L, Pineau J, Courville A, Bengio Y (2017) A hierarchical latent variable encoder-decoder model for generating dialogues. In: Thirty-First AAAI conference on artificial intelligence

    Google Scholar 

  24. Sinaceur M, Tiedens LZ (2006) Get mad and get more than even: when and why anger expression is effective in negotiations

    Google Scholar 

  25. Tuyen NTV, Jeong S, Chong NY (2018) Emotional bodily expressions for culturally competent robots through long term human-robot interaction. In: 2018 IEEE/RSJ international conference on intelligent robots and systems, pp 2008–2013

    Google Scholar 

  26. Wang X, Shi W, Kim R, Oh Y, Yang S, Zhang J, Yu Z (2019) Persuasion for good: towards a personalized persuasive dialogue system for social good. In: Proceedings of ACL

    Google Scholar 

  27. Watanabe M, Ogawa K, Ishiguro H (2018) At the department store—can androids be a social entity in the real world? In: Geminoid studies, pp 423–427

    Google Scholar 

  28. Wilson E (2003) Perceived effectiveness of interpersonal persuasion strategies in computer-mediated communication. Comput Hum Behav 19(5):537–552

    Article  Google Scholar 

  29. Yoshino K, Ishikawa Y, Mizukami M, Suzuki Y, Sakti S, Nakamura S (2018) Dialogue scenario collection of persuasive dialogue with emotional expressions via crowdsourcing. In: Proceedings of the 11th language resources and evaluation conference

    Google Scholar 

  30. Zhou H, Huang M, Zhang T, Zhu X, Liu B (2017) Emotional chatting machine: emotional conversation generation with internal and external memory. In: AAAI

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sara Asai .

Editor information

Editors and Affiliations

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

Asai, S., Yoshino, K., Shinagawa, S., Sakti, S., Nakamura, S. (2022). Eliciting Cooperative Persuasive Dialogue by Multimodal Emotional Robot. 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_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5538-9_10

  • 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