Abstract
Endowing a chatbot with the capability of specific emotion expression will significantly improve both chatbot’s usability and users’ satisfaction. Recently, many studies on open-domain neural emotional, conversational models (chatbots) have been conducted. However, enabling a chatbot to control what kind of emotion to respond to in conversation explicitly is still under exploration. This paper proposes a novel affective chatbot based on the sequence-to-sequence framework, responding with appropriate emotion like a human. In particular, a new module called single emotion generator is designed in the new chatbot model to address the existing issue of controlling over reacting emotion. It enables the chatbot to select the appropriate emotion for a response when interacting with users. In the decoder, an affective lexicon-based method generates emotion-awareness responses based on the specific emotion controlled by the single emotion generator. The proposed chatbot outperforms mainstream baseline algorithms for both semantic fluency and emotion consistence metrics through experimental evaluation. The experimental results also demonstrate that the new chatbot obtains the ability to control the emotion for response explicitly and responds emotionally with the specific emotion.
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References
Huang C, Zaiane O R. Generating responses expressing emotion in an open-domain dialogue system. In: Proceedings of International Conference on Internet Science, 2019. 100–112
Zhang R, Wang Z Y, Mai D C. Building emotional conversation systems using multi-task Seq 2Seq learning. In: Proceedings of Natural Language Processing and Chinese Computing, 2018. 612–621
Picard R W. Affective Computing. Cambridge: The MIT Press, 1997
Ochs M, Pelachaud C, Sadek D. An empathic virtual dialog agent to improve human-machine interaction. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, 2008. 89–96
Prendinger H, Ishizuka M. The empathic companion: a character-based interface that addresses users’ affective states. Appl Artif Intell, 2005, 19: 267–285
Zhou H, Huang M L, Zhang T Y, et al. Emotional chatting machine: emotional conversation generation with internal and external memory. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018. 730–738
Colombo P, Witon W, Modi A, et al. Affect-driven dialog generation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019. 3734–3743
Song Z Q, Zheng X Q, Liu L, et al. Generating responses with a specific emotion in dialog. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019. 3685–3695
Shen L, Feng Y. CDL: curriculum dual learning for emotion-controllable response generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. 556–566
Zhong P X, Wang D, Miao C Y. An affect-rich neural conversational model with biased attention and weighted cross-entropy loss. In: Proceedings of the AAAI Conference on Artifficial Intelligence, 2019, 33: 7492–7500
Sayan G, Mathieu C, Eugene L, et al. Affect-LM: a neural language model for customizable affective text generation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, 2017. 634–642
Lin Z J, Xu P, Winata G I, et al. CAiRE: an end-to-end empathetic chatbot. In: Proceedings of the AAAI Conference on Artifficial Intelligence, 2020. 34: 13622–13623
Gao B B, Xing C, Xie C W, et al. Deep label distribution learning with label ambiguity. IEEE Trans Image Process, 2017, 26: 2825–2838
Marsella S, Gratch J. Computationally modeling human emotion. Commun ACM, 2014, 57: 56–67
Ma Y K, Nguyen K L, Xing F Z, et al. A survey on empathetic dialogue systems. Inf Fusion, 2020, 64: 50–70
Busemeyer J R, Dimperio E, Jessup R K. Integrating emotional processes into decision-making models. In: Integrated Models of Cognitive Systems. Oxford Scholarship Online, 2007. 213–229
McTear M F, Callejas Z, Griol D. The Conversational Interface. Berlin: Springer Publishing Company, Incorporated, 2016
Wang Z X, Ho S B, Cambria E. A review of emotion sensing: categorization models and algorithms. Multimed Tools Appl, 2020, 79: 35553–35582
Savery R, Weinberg G. A survey of robotics and emotion: classifications and models of emotional interaction. In: Proceedings of the 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020. 986–993
Soleymani M, Lichtenauer J, Pun T, et al. A multimodal database for affect recognition and implicit tagging. IEEE Trans Affective Comput, 2012, 3: 42–55
Cambria E, Fu J, Bisio F, et al. AffectiveSpace 2: Enabling Affective Intuition for Concept-level Sentiment Analysis. Austin: AAAI Press, 2015. 508–514
Lee G G, Kim H K, Jeong M, et al. Natural Language Dialog Systems and Intelligent Assistants. Berlin: Springer Publishing Company, Incorporated, 2015
Ma Y K, Peng H Y, Khan T, et al. Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput, 2018, 10: 639–650
Peng H Y, Ma Y K, Li Y, et al. Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowledge-Based Syst, 2018, 148: 167–176
Zhang Y X, Fu J M, She D Y, et al. Text emotion distribution learning via multi-task convolutional neural network. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018. 4595–4601
Prendinger H, Mori J, Ishizuka M. Using human physiology to evaluate subtle expressivity of a virtual quizmaster in a mathematical game. Int J Human-Comput Studies, 2005, 62: 231–245
Marcin S. Affect listeners: acquisition of affective states by means of conversational systems. In: Development of Multimodal Interfaces: Active Listening and Synchrony. Berlin: Springer, 2010. 169–181
Fitzpatrick K K, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health, 2017, 4: e19
Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014. 3104–3112
Dziri N, Kamalloo E, Mathewson K W, et al. Augmenting neural response generation with context-aware topical attention. In: Proceedings of the 1st Workshop on NLP for Conversational AI, 2019. 18–31
Hancock B, Bordes A, Mazare P-E, et al. Learning from dialogue after deployment: feed yourself, chatbot! In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019. 3667–3684
Wu Y, Wei F R, Huang S H, et al. Response generation by context-aware prototype editing. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 7281–7288
Qian Q, Huang M L, Zhao H Z, et al. Assigning personality/profile to a chatting machine for coherent conversation generation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence Main track, 2018. 4279–4285
Zhong P X, Zhang C, Wang H, et al. Towards persona-based empathetic conversational models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. 6556–6566
Mehrabian A. Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in Temperament. Curr Psychol, 1996, 14: 261–292
Zhong P X, Wang D, Li P F, et al. CARE: commonsense-aware emotional response generation with latent concepts. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. 35: 14577–14585
Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations, 2015. 1–15
Poria S, Majumder N, Mihalcea R, et al. Emotion recognition in conversation: research challenges, datasets, and recent advances. IEEE Access, 2019, 7: 100943–100953
Rolls E T, Ekman P, Perrett D I, et al. Facial expressions of emotion: an old controversy and new findings. Phil Trans Roy Soc Lond B, 1992, 335: 63–69
Cho K, Merrienboer B V, Bahdanau D, et al. On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of the 8th Workshop on Syntax, Semantics and Structure in Statistical Translation, 2014. 103–111
Seyeditabari A, Tabari N, Gholizadeh S, et al. Emotion detection in text: focusing on latent representation. 2019. ArXiv:1907.09369
Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016. 207–212
Gross J J. The emerging field of emotion regulation: an integrative review. Rev General Psychol, 1998, 2: 271–299
Hochschild A R. Emotion work, feeling rules, and social structure. Am J Sociol, 1979, 85: 551–575
Alam F, Danieli M, Riccardi G. Annotating and modeling empathy in spoken conversations. Comput Speech Language, 2018, 50: 40–61
Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 2015. 1412–1421
Xu L H, Lin H F, Pan Y, et al. Constructing the affective lexicon ontology (in Chinese). J China Soc Sci Tech Inf, 2008, 2: 180–185
Nakamura R, Sudoh K, Yoshino K, et al. Another diversity-promoting objective function for neural dialogue generation. In: Proceedings of AAAI 2019 Workshop on Reasoning and Learning for Human-Machine Dialogues (DEEP-DIAL 2019), 2019
Seyeditabari A, Tabari N, Gholizadeh S, et al. Emotional embeddings: refining word embeddings to capture emotional content of words. 2019. ArXiv:1906.00112
Song Y, Shi S M, Li J, et al. Directional skip-gram: explicitly distinguishing left and right context for word embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018. 2: 175–180
Yang J F, She D Y, Sun M. Joint image emotion classification and distribution learning via deep convolutional neural network. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence Main track, 2017. 3266–3272
Chen B X, Cherry C. A systematic comparison of smoothing techniques for sentence-level BLEU. In: Proceedings of the 9th Workshop on Statistical Machine Translation, Baltimore, 2014. 362–367
Fleiss J L. Measuring nominal scale agreement among many raters. Psychol Bull, 1971, 76: 378–382
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This work was supported in part by National Key Research and Development Program of China (Grant No. 2019YFF0302601).
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Jiang, C., Zhang, C., Ji, Y. et al. An affective chatbot with controlled specific emotion expression. Sci. China Inf. Sci. 65, 202102 (2022). https://doi.org/10.1007/s11432-020-3356-4
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DOI: https://doi.org/10.1007/s11432-020-3356-4