research-article

Generating Stylistic and Personalized Dialogues for Virtual Agents in Narratives

Authors:

Charlie HargoodAuthors Info & Claims

AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

Pages 737 - 746

Published: 30 May 2023 Publication History

Abstract

Virtual agents interact with each other through dialogues in various types of narratives (e.g. narrative films). In this paper, we propose an approach on the basis of DialoGPT pre-trained language model, which explores the impact of dialogue generation with different levels of agents' personalities derived from narrative films based on the Big-Five model, as well as with three different embedding methods. From the experimental results using automatic metrics and human judgments, we investigate and analyze the impact of different settings on narrative dialogue generation. Also, we demonstrate that our approach is able to generate dialogues with increased variety that correctly reflect the corresponding target personality.

References

[1]

Monika Bednarek. 2017. The role of dialogue in fiction. Pragmatics of fiction (2017), 129--158.

[2]

Sven Buechel, Susanna Rücker, and Udo Hahn. 2020. Learning and Evaluating Emotion Lexicons for 91 Languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1202--1217.

[3]

Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. 2018. Universal Sentence Encoder for English. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 169--174.

[4]

Seymour Chatman. 1978. Story and Discourse: Narrative Structure in Fiction and Film. Cornell University Press. https://doi.org/

[5]

Pierre Colombo, Wojciech Witon, Ashutosh Modi, James Kennedy, and Mubbasir Kapadia. 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, Volume 1 (Long and Short Papers). 3734--3743.

[6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[7]

Jessica Ficler and Yoav Goldberg. 2017. Controlling Linguistic Style Aspects in Neural Language Generation. In Proceedings of the Workshop on Stylistic Variation. 94--104.

[8]

Albert Gatt and Emiel Krahmer. 2018. Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, Vol. 61 (2018), 65--170.

Digital Library

[9]

Pablo Gervás. 2010. Engineering linguistic creativity: Bird flight and jet planes. In Proceedings of the NAACL HLT 2010 Second Workshop on Computational Approaches to Linguistic Creativity. Association for Computational Linguistics, 23--30.

[10]

Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. 2017. Affect-LM: A Neural Language Model for Customizable Affective Text Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 634--642.

[11]

Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017. Toward controlled generation of text. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 1587--1596.

Digital Library

[12]

Chenyang Huang, Osmar R Zaiane, Amine Trabelsi, and Nouha Dziri. 2018. Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 49--54.

[13]

O.P. John and S. Srivastava. 1999. The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. In Handbook of personality: Theory and research. Guilford Press, 102--138.

[14]

Sarah Kozloff. 2000. Overhearing film dialogue. Univ of California Press.

[15]

Wilkins Leong, Julie Porteous, and John Thangarajah. 2022. Automated Story Sifting Using Story Arcs. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. 1669--1671.

[16]

Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. A Persona-Based Neural Conversation Model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 994--1003.

[17]

Chia-Wei Liu, Ryan Lowe, Iulian Vlad Serban, Mike Noseworthy, Laurent Charlin, and Joelle Pineau. 2016. 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. 2122--2132.

[18]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692 (2019).

[19]

Francc ois Mairesse and Marilyn Walker. 2007. PERSONAGE: Personality generation for dialogue. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 496--503.

[20]

Francc ois Mairesse and Marilyn A Walker. 2011. Controlling user perceptions of linguistic style: Trainable generation of personality traits. Computational Linguistics, Vol. 37, 3 (2011), 455--488.

Digital Library

[21]

Francc ois Mairesse, Marilyn A Walker, Matthias R Mehl, and Roger K Moore. 2007. Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of artificial intelligence research, Vol. 30 (2007), 457--500.

Digital Library

[22]

Jamie Matthews, Fred Charles, Julie Porteous, and Alexandra Mendes. 2017. MISER: Mise-en-scène region support for staging narrative actions in interactive storytelling. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 782--790.

[23]

Pierre-Emmanuel Mazare, Samuel Humeau, Martin Raison, and Antoine Bordes. 2018. Training Millions of Personalized Dialogue Agents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2775--2779.

[24]

Robert McKee. 1997. Story: Style, Structure, Substance, and the Principles of Screenwriting. New York: HarperCollins.

[25]

Robert McKee. 2016. Dialogue: The art of verbal action for page, stage, and screen. Hachette UK.

[26]

OpenAI. 2022. ChatGPT: Optimizing Language Models for Dialogue. https://openai.com/blog/chatgpt/. Accessed on Feburary 23, 2023.

[27]

Shereen Oraby, Lena Reed, Shubhangi Tandon, TS Sharath, Stephanie Lukin, and Marilyn Walker. 2018. Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue. 180--190.

[28]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 311--318.

[29]

Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of NAACL-HLT. 2227--2237.

[30]

Mihai Polceanu, Julie Porteous, Alan Lindsay, and Marc Cavazza. 2020. Narrative Plan Generation with Self-Supervised Learning. In Thirty-Fifth AAAI Conference on Artificial Intelligence. AAAI Press.

[31]

Julie Porteous, Marc Cavazza, and Fred Charles. 2010. Narrative generation through characters' point of view. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1. International Foundation for Autonomous Agents and Multiagent Systems, 1297--1304.

[32]

Julie Porteous, Fred Charles, and Marc Cavazza. 2013. NetworkING: using character relationships for interactive narrative generation. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems, 595--602.

[33]

Julie Porteous, Fred Charles, and Marc Cavazza. 2016. Plan-based narrative generation with coordinated subplots. In Proceedings of the Twenty-second European Conference on Artificial Intelligence. IOS Press, 846--854.

Digital Library

[34]

Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018. Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation. In IJCAI. 4279--4285.

[35]

Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. https://s3-us-west-2. amazonaws. com/openai-assets/research-covers/languageunsupervised/language understanding paper. pdf.

[36]

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. 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).

[37]

S. Rimmon-Kenan. 1983. Narrative Fiction: Contemporary Poetics. Routledge. 82018859 https://books.google.co.uk/books?id=PqzWemM8C3cC

[38]

James A Russell. 2003. Core affect and the psychological construction of emotion. Psychological review, Vol. 110, 1 (2003), 145.

[39]

Siqi Shen and Charles Welch. 2020. Counseling-style reflection generation using generative pretrained transformers with augmented context. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue.

[40]

Haoyu Song, Weinan Zhang, Yiming Cui, Dong Wang, and Ting Liu. 2019. Exploiting Persona Information for Diverse Generation of Conversational Responses. CoRR, Vol. abs/1905.12188 (2019). arxiv: 1905.12188 http://arxiv.org/abs/1905.12188

[41]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998--6008.

[42]

Weixuan Wang, Xiaoling Cai, Chong Hsuan Huang, Haoran Wang, Haonan Lu, Ximing Liu, and Wei Peng. 2021. Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona. arXiv preprint arXiv:2109.08875 (2021).

[43]

David R Winer and R Michael Young. 2017. Automated Screenplay Annotation for Extracting Storytelling Knowledge. In Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference.

[44]

Weilai Xu, Fred Charles, Charlie Hargood, Feng Tian, and Wen Tang. 2020. Influence of Personality-Based Features for Dialogue Generation in Computational Narratives. In ECAI 2020 - 24th European Conference on Artificial Intelligence (Frontiers in Artificial Intelligence and Applications, Vol. 325). IOS Press, 2945--2946. https://doi.org/10.3233/FAIA200466

[45]

Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, and Zhoujun Li. 2020. StyleDGPT: Stylized Response Generation with Pre-trained Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. 1548--1559.

[46]

R Michael Young, Stephen G Ware, Brad A Cassell, and Justus Robertson. 2013. Plans and planning in narrative generation: a review of plan-based approaches to the generation of story, discourse and interactivity in narratives. Sprache und Datenverarbeitung, Special Issue on Formal and Computational Models of Narrative, Vol. 37, 1--2 (2013), 41--64.

[47]

Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 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 (Volume 1: Long Papers). 2204--2213.

[48]

Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and William B Dolan. 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. 270--278.

[49]

Yinhe Zheng, Rongsheng Zhang, Minlie Huang, and Xiaoxi Mao. 2020. A pre-training based personalized dialogue generation model with persona-sparse data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9693--9700.

[50]

Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao. 2020. Towards Persona-Based Empathetic Conversational Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 6556--6566.

Cited By

Christiansen FHollensberg LJensen NJulsgaard KJespersen KNikolov I(2024)Exploring Presence in Interactions with LLM-Driven NPCs: A Comparative Study of Speech Recognition and Dialogue OptionsProceedings of the 30th ACM Symposium on Virtual Reality Software and Technology10.1145/3641825.3687716(1-11)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3641825.3687716
Ha JJeon HHan DSeo JOh C(2024)CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642472(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642472
Wang PMiller MQueiroz ABailenson J(2024)Socially Late, Virtually Present: The Effects of Transforming Asynchronous Social Interactions in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642244(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642244

Index Terms

Generating Stylistic and Personalized Dialogues for Virtual Agents in Narratives
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics

Recommendations

Towards Generating Stylistic Dialogues for Narratives Using Data-Driven Approaches
Interactive Storytelling
Abstract
Recently, there has been a renewed interest in generating dialogues for narratives. Within narrative dialogues, their structure and content are essential, though style holds an important role as a mean to express narrative dialogue through telling ...
Using Virtual Narratives to Explore Children's Story Understanding
AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

Interactive Narratives are systems that use automated narrative generation techniques to create multiple story variants which can be shown to an audience, as virtual narratives, using cinematic staging techniques. Previous research in this area has ...
Politeness and alignment in dialogues with a virtual guide
AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1

Language alignment is something that happens automatically in dialogues between human speakers. The ability to align is expected to increase the believability of virtual dialogue agents. In this paper we extend the notion of alignment to affective ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

May 2023

3131 pages

ISBN:9781450394321

General Chairs:
Noa Agmon
Bar-Ilan University, Israel
,
Bo An
Nanyang Technological University, Singapore
,
Program Chairs:
Alessandro Ricci
University of Bologna, Italy
,
William Yeoh
Washington University in St. Louis, USA

Sponsors

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 30 May 2023

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AAMAS '23

Sponsor:

SIGAI

AAMAS '23: International Conference on Autonomous Agents and Multiagent Systems

May 29 - June 2, 2023

London, United Kingdom

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
75
Total Downloads

Downloads (Last 12 months)39
Downloads (Last 6 weeks)1

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Christiansen FHollensberg LJensen NJulsgaard KJespersen KNikolov I(2024)Exploring Presence in Interactions with LLM-Driven NPCs: A Comparative Study of Speech Recognition and Dialogue OptionsProceedings of the 30th ACM Symposium on Virtual Reality Software and Technology10.1145/3641825.3687716(1-11)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3641825.3687716
Ha JJeon HHan DSeo JOh C(2024)CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642472(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642472
Wang PMiller MQueiroz ABailenson J(2024)Socially Late, Virtually Present: The Effects of Transforming Asynchronous Social Interactions in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642244(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642244

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents