Skip to main content

Advertisement

SpringerLink
Log in

Recognizing Emotion Cause in Conversations

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause/effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset. Our transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches on our dataset. We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. pronounced as reckon.

  2. By “causal span from evidence in the context” we mean a causal span from the conversation history \(H(U_t)\).

References

  1. Ameer I, Ashraf N, Sidorov G, Adorno HG. Multi-label emotion classification using content-based features in Twitter. Computación y Sistemas. 2020;24(3):1159–64.

    Article  Google Scholar 

  2. Brandsen A, Verberne S, Wansleeben M, Lambers K. Creating a dataset for named entity recognition in the archaeology domain. In Proceedings of the 12th Language Resources and Evaluation Conference (Marseille, France, May 2020), European Language Resources Association. 2020. pp. 4573–4577.

  3. Busso C, Bulut M, Lee C-C, Kazemzadeh A, Mower E, Kim S, Chang JN, Lee S, Narayanan SS. IEMOCAP: Interactive emotional dyadic motion capture database. Lang Resour Eval. 2008;42(4):335–59.

    Article  Google Scholar 

  4. Chakrabarty T, Hidey C, Muresan S, McKeown K, Hwang A. AMPERSAND: Argument mining for PERSuAsive oNline discussions. 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) (Hong Kong, China, Nov. 2019), Association for Computational Linguistics. 2019. pp. 2933–2943.

  5. Chen X, Li Q, Wang J. Conditional causal relationships between emotions and causes in texts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Online, Nov. 2020), Association for Computational Linguistics. 2020 pp. 3111–3121.

  6. Chen Y, Hou W, Cheng X, Li S. Joint learning for emotion classification and emotion cause detection. In Riloff E, Chiang D, Hockenmaier J, Tsujii J, Editors. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 – November 4, 2018. Association for Computational Linguistics; 2018. pp. 646–651.

  7. Chen Y, Lee SYM, Li S, Huang CR. Emotion cause detection with linguistic constructions. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) (Beijing, China, Aug. 2010), Coling 2010 Organizing Committee. 2010. pp. 179–187.

  8. Choi Y, Cardie C, Riloff E, Patwardhan S. Identifying sources of opinions with conditional random fields and extraction patterns. In HLT/EMNLP 2005, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 6-8 October 2005, Vancouver, British Columbia, Canada (2005), The Association for Computational Linguistics. 2005. pp. 355–362.

  9. Colneriĉ N, Demsar J. Emotion recognition on twitter: comparative study and training a unison model. IEEE Trans Affect Comput. 2018.

  10. Das D, Bandyopadhyay S. Finding emotion holder from Bengali blog Texts—An unsupervised syntactic approach. In Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation (Tohoku University, Sendai, Japan, Nov. 2010), Institute of Digital Enhancement of Cognitive Processing, Waseda University. 2010. pp. 621–628.

  11. Devlin J, Chang M, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein J, Doran C, Solorio T, Editors. 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) (2019), Association for Computational Linguistics; 2019. pp. 4171–4186.

  12. Ding Z, Xia R, Yu J. ECPE-2D: Emotion-cause pair extraction based on joint two-dimensional representation, interaction and prediction. In Jurafsky D, Chai J, Schluter N, and Tetreault JR, Editors. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Association for Computational Linguistics; 2020. pp. 3161–3170.

  13. Ding Z, Xia R, Yu J. End-to-end emotion-cause pair extraction based on sliding window multi-label learning. In Webber B, Cohn T, He Y, Liu Y, Editors. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics; 2020 pp. 3574–3583.

  14. Dragoni M, Donadello I, Cambria E. OntoSenticNet 2: Enhancing reasoning within sentiment analysis. IEEE Intell Syst. 2021;36:5.

    Google Scholar 

  15. Ekman P. Facial expression and emotion. Am Psychol. 1993;48(4):384.

    Article  Google Scholar 

  16. Ellsworth PC, Scherer KR. Appraisal processes in emotion. Oxford University Press. 2003. pp 572–595.

  17. Gao Q, Jiannan H, Ruifeng X, Lin G, He Y, Wong K, Lu Q. Overview of ntcir-13 eca task. In Proceedings of the NTCIR-13 Conference. 2017.

  18. Ghazi D, Inkpen D, Szpakowicz S. Detecting emotion stimuli in emotion-bearing sentences. In Gelbukh AF, Editor. Computational Linguistics and Intelligent Text Processing – 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II of Lecture Notes in Computer Science, Springer; 2015.  vol 9042 pp 152–165.

  19. Ghosal D, Majumder N, Mihalcea R, Poria S. Utterance-level dialogue understanding: An empirical study. 2020.

  20. Gui L, Wu D, Xu R, Lu Q, Zhou Y. Event-driven emotion cause extraction with corpus construction. In EMNLP (2016), World Scientific. 2016 pp. 1639–1649.

  21. Gui L, Yuan L, Xu R, Liu B, Lu Q, Zhou Y. Emotion cause detection with linguistic construction in Chinese Weibo text. In Zong C, Nie J, Zhao D, Feng Y, Editors. Natural Language Processing and Chinese Computing – Third CCF Conference, NLPCC 2014, Shenzhen, China, December 5-9, 2014. Proceedings of Communications in Computer and Information Science, Springer; 2014. vol 496 pp 457–464.

  22. Izard CE. Basic emotions, relations among emotions, and emotion-cognition relations. Psychol Rev. 1992;99(3):561–5.

    Article  Google Scholar 

  23. Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O. SpanBERT: Improving pre-training by representing and predicting spans. 2020.

  24. Kratzwald B, Ilic S, Kraus M, Feuerriegel S, Prendinger H. Decision support with text-based emotion recognition: Deep learning for affective computing. 2018. arXiv preprint arXiv:1803.06397.

  25. Lee, SYM, Chen Y, Huang CR. A text-driven rule-based system for emotion cause detection. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (Los Angeles, CA, June 2010), Association for Computational Linguistics. 2010. pp. 45–53.

  26. Levy O, Seo M, Choi E, Zettlemoyer L. Zero-shot relation extraction via reading comprehension. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) (Vancouver, Canada, Aug. 2017), Association for Computational Linguistics. 2017. pp. 333–342.

  27. Li X, Feng J, Meng Y, Han Q, Wu F, Li J. A unified MRC framework for named entity recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Online, July 2020), Association for Computational Linguistics. 2020. pp. 5849–5859.

  28. Li Y, Su H, Shen X, Li W, Cao Z, and Niu S. Dailydialog: A manually labelled multi-turn dialogue dataset. In Kondrak G, Watanabe T, Editors. 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. 2017. pp. 986–995.

  29. Liu, B. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers. 2012.

  30. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. RoBERTa: A robustly optimized BERT pretraining approach. 2019. arXiv preprint arXiv:1907.11692.

  31. Moreno Jiménez LG, Torres Moreno JM. LiSSS: A new corpus of literary Spanish sentences for emotions detection. Computación y Sistemas. 2020;24(3):1139–47.

    Article  Google Scholar 

  32. Neviarouskaya A, Aono M. Extracting causes of emotions from text. In Sixth International Joint Conference on Natural Language Processing, IJCNLP 2013, Nagoya, Japan, October 14-18, 2013, Asian Federation of Natural Language Processing / ACL. 2013. pp. 932–936.

  33. Plutchik R. A psychoevolutionary theory of emotions. Social Science Information. 1982;21(4–5):529–53.

    Article  Google Scholar 

  34. Rajpurkar P, Zhang J, Lopyrev K, Liang P. Squad: 100,000+ questions for machine comprehension of text. 2016.

  35. Talmy L. Toward a cognitive semantics. MIT press. 2000;2.

  36. Wei P, Zhao J, Mao W. Effective inter-clause modeling for end-to-end emotion-cause pair extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Online, July 2020), Association for Computational Linguistics. 2020. pp. 3171–3181.

  37. Xia R, Ding Z. Emotion-cause pair extraction: A new task to emotion analysis in texts. In Korhonen A, Traum DR, Màrquez L, Editors. 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; 2019. pp. 1003–1012.

  38. Zajonc RB. Feeling and thinking: Preferences need no inferences. American Psychologist. 1980. 151–175.

Download references

Author information

Authors and Affiliations

  1. Singapore University of Technology and Design, Singapore, Singapore

    Soujanya Poria, Navonil Majumder, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian & Pengfei Hong

  2. National University of Singapore, Singapore, Singapore

    Devamanyu Hazarika

  3. Independent researcher, India, India

    Romila Ghosh

  4. Nanyang Technological University, Singapore, Singapore

    Abhinaba Roy

  5. Adobe Research, India, India

    Niyati Chhaya

  6. CIC, Instituto Politécnico Nacional, Mexico, Mexico

    Alexander Gelbukh

  7. University of Michigan, Michigan, USA

    Rada Mihalcea

Authors
  1. Soujanya Poria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Navonil Majumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Devamanyu Hazarika
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deepanway Ghosal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rishabh Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samson Yu Bai Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pengfei Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Romila Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Abhinaba Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Niyati Chhaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alexander Gelbukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rada Mihalcea
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Gelbukh.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors

Informed Consent

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Additional information

S. Poria, N. Majumder, D. Ghosal, R. Bhardwaj, S. Yu Bai Jian, and P. Hong have received support from the A*STAR under its RIE 2020 Advanced Manufacturing and Engineering programmatic grant, Award No. A19E2b0098. A. Gelbukh has received support from the Mexican Government through the grant A1-S-47854 of the CONACYT, Mexico, and grants 20211784, 20211884, and 20211178 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poria, S., Majumder, N., Hazarika, D. et al. Recognizing Emotion Cause in Conversations. Cogn Comput 13, 1317–1332 (2021). https://doi.org/10.1007/s12559-021-09925-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09925-7

Search

Navigation