skip to main content
survey

Computational Politeness in Natural Language Processing: A Survey

Published:08 May 2024Publication History
Skip Abstract Section

Abstract

Computational approach to politeness is the task of automatically predicting and/or generating politeness in text. This is a pivotal task for conversational analysis, given the ubiquity and challenges of politeness in interactions. The computational approach to politeness has witnessed great interest from the conversational analysis community. This article is a compilation of past works in computational politeness in natural language processing. We view four milestones in the research so far, viz. supervised and weakly supervised feature extraction to identify and induce politeness in a given text, incorporation of context beyond the target text, study of politeness across different social factors, and study the relationship between politeness and various socio-linguistic cues. In this article, we describe the datasets, approaches, trends, and issues in computational politeness research. We also discuss representative performance values and provide pointers to future works, as given in the prior works. In terms of resources to understand the state of the art, this survey presents several valuable illustrations—most prominently, a table summarizing the past papers along different dimensions, such as the types of features, annotation techniques, and datasets used.

REFERENCES

  1. [1] Alexandrov Mikhail, Blanco Xavier, Ponomareva Natalia, and Rosso Paolo. 2007. Constructing empirical models for automatic dialog parameterization. In International Conference on Text, Speech and Dialogue. Springer, 455463.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Alexandrov Mikhail, Ponomareva Natalia, and Blanco Xavier. 2008. Regression model for politeness estimation trained on examples. In Proceedings of the NooJ’07 Conference. 206–13.Google ScholarGoogle Scholar
  3. [3] Aljanaideh Ahmad, Fosler-Lussier Eric, and Marneffe Marie-Catherine de. 2020. Contextualized Embeddings for Enriching Linguistic Analyses on Politeness. In Proceedings of the 28th International Conference on Computational Linguistics. 21812190.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Aubakirova Malika and Bansal Mohit. 2016. Interpreting neural networks to improve politeness comprehension. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 20352041.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Bahdanau Dzmitry, Cho Kyung Hyun, and Bengio Yoshua. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR’15).Google ScholarGoogle Scholar
  6. [6] Bao Jiajun, Wu Junjie, Zhang Yiming, Chandrasekharan Eshwar, and Jurgens David. 2021. Conversations gone alright: Quantifying and predicting prosocial outcomes in online conversations. In Proceedings of the Web Conference. 11341145.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Barandela Ricardo, Sánchez José Salvador, Garcıa Vicente, and Rangel Edgar. 2003. Strategies for learning in class imbalance problems. Pattern Recogn. 36, 3 (2003), 849851.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Elizabeth Bates and Louise Silvern. 1977. Social adjustment and politeness in preschoolers. Journal of Communication 27, 2 (1977), 104–111.Google ScholarGoogle Scholar
  9. [9] Bharti Prabhat Kumar, Navlakha Meith, Agarwal Mayank, and Ekbal Asif. 2023. PolitePEER: Does peer review hurt? A dataset to gauge politeness intensity in the peer reviews. Lang. Resourc. Eval. (2023), 123.Google ScholarGoogle Scholar
  10. [10] Biber Douglas. 1991. Variation across Speech and Writing. Cambridge University Press.Google ScholarGoogle Scholar
  11. [11] Blum Avrim L. and Furst Merrick L.. 1997. Fast planning through planning graph analysis. Artif. Intell. 90, 1-2 (1997), 281300.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Bothe Chandrakant and Wermter Stefan. 2022. Conversational analysis of daily dialog data using polite emotional dialogue acts. In Proceedings of the 13th Language Resources and Evaluation Conference. 23952400.Google ScholarGoogle Scholar
  13. [13] Bousfield Derek. 2008. Impoliteness in Interaction. John Benjamins, Amsterdam, 1295.Google ScholarGoogle Scholar
  14. [14] Brown Jonathon D. and Smart S.. 1991. The self and social conduct: Linking self-representations to prosocial behavior. J. Pers. Soc. Psychol. 60, 3 (1991), 368.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Brown Penelope and Fraser Colin. 1979. Speech as a marker of situation. In Social Markers in Speech. Cambridge University Press, 3362.Google ScholarGoogle Scholar
  16. [16] Brown Penelope and Levinson Stephen C.. 1978. Universals in language usage: Politeness phenomena. In Questions and Politeness: Strategies in Social Interaction. Cambridge University Press, 56311.Google ScholarGoogle Scholar
  17. [17] Brown Penelope, Levinson Stephen C., and Levinson Stephen C.. 1987. Politeness: Some Universals in Language Usage. Vol. 4. Cambridge University Press.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Brown Peter F., Pietra Stephen A. Della, Pietra Vincent J. Della, Lai Jennifer C., and Mercer Robert L.. 1992. An estimate of an upper bound for the entropy of English. Comput. Ling. 18, 1 (1992), 3140.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Brown Tom, Mann Benjamin, Ryder Nick, Subbiah Melanie, Kaplan Jared D., Dhariwal Prafulla, Neelakantan Arvind, Shyam Pranav, Sastry Girish, Askell Amanda, et al. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, Vol. 33, 18771901.Google ScholarGoogle Scholar
  20. [20] Buechel Sven, Buffone Anneke, Slaff Barry, Ungar Lyle, and Sedoc João. 2018. Modeling empathy and distress in reaction to news stories. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 47584765.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Byrne Bill, Krishnamoorthi Karthik, Sankar Chinnadhurai, Neelakantan Arvind, Goodrich Ben, Duckworth Daniel, Yavuz Semih, Dubey Amit, Kim Kyu-Young, and Cedilnik Andy. 2019. Taskmaster-1: Toward a realistic and diverse dialog dataset. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 45164525.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Carstensen Laura L., Turan Bulent, Scheibe Susanne, Ram Nilam, Ersner-Hershfield Hal, Samanez-Larkin Gregory R., Brooks Kathryn P., and Nesselroade John R.. 2011. Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychol. Aging 26, 1 (2011), 21.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Chen Guimin, Tian Yuanhe, and Song Yan. 2020. Joint aspect extraction and sentiment analysis with directional graph convolutional networks. In Proceedings of the 28th International Conference on Computational Linguistics. 272279.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Chhaya Niyati, Chawla Kushal, Goyal Tanya, Chanda Projjal, and Singh Jaya. 2018. Frustrated, polite, or formal: Quantifying feelings and tone in email. In Proceedings of the 2nd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. 7686.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Christie. 2007. Relevance theory and politeness. Journal of Politeness Research 3, 2 (2007), 269–294. Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2024. Scaling instruction-finetuned language models. Journal of Machine Learning Research 25, 70 (2024), 1–53. Retrieved from http://jmlr.org/papers/v25/23-0870.htmlGoogle ScholarGoogle Scholar
  27. [27] Chung Yi-Ling, Kuzmenko Elizaveta, Tekiroğlu Serra Sinem, and Guerini Marco. 2019. CONAN-COunter NArratives through Nichesourcing: A multilingual dataset of responses to fight online hate speech. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 28192829.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Culpeper Jonathan. 2011. Politeness and impoliteness. In Pragmatics of Society. de Gruyter Mouton, 393.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Culpeper Jonathan. 2011. Impoliteness: Using Language to Cause Offence, Vol. 28. Cambridge University Press.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Culpeper Jonathan. 2016. Impoliteness strategies. In Interdisciplinary Studies in Pragmatics, Culture and Society, 421445.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Culpeper Jonathan, Haugh Michael, and Kádár Dániel Z.. 2017. The Palgrave Handbook of Linguistic (im) Politeness. Springer.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Dainton Marianne, Stafford Laura, and Canary Daniel J.. 1994. Maintenance strategies and physical affection as predictors of love, liking, and satisfaction in marriage. Commun. Rep. 7, 2 (1994), 8898.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Danescu-Niculescu-Mizil Cristian, Sudhof Moritz, Jurafsky Dan, Leskovec Jure, and Potts Christopher. 2013. A computational approach to politeness with application to social factors. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 250259.Google ScholarGoogle Scholar
  34. [34] Dasgupta Tirthankar, Sinha Manjira, and Praveen Chundru Geetha. 2023. Graph induced transformer network for detection of politeness and formality in text. In Companion Proceedings of the ACM Web Conference 2023. 221224.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Devlin Jacob, Chang Ming-Wei, Lee Kenton, and Toutanova Kristina. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https://arxiv.org/abs/1810.04805Google ScholarGoogle Scholar
  36. [36] Dippold Doris, Lynden Jenny, Shrubsall Rob, and Ingram Rich. 2020. A turn to language: How interactional sociolinguistics informs the redesign of prompt: Response chatbot turns. Discourse Context Media 37 (2020), 100432.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Du Chunning, Sun Haifeng, Wang Jingyu, Qi Qi, Liao Jianxin, Wang Chun, and Ma Bing. 2019. Investigating capsule network and semantic feature on hyperplanes for text classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 456465.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Feely Weston, Hasler Eva, and Gispert Adrià de. 2019. Controlling Japanese honorifics in English-to-Japanese neural machine translation. In Proceedings of the 6th Workshop on Asian Translation. 4553.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Feng Shutong, Lubis Nurul, Geishauser Christian, Lin Hsien-Chin, Heck Michael, Niekerk Carel van, and Gasic Milica. 2022. EmoWOZ: A large-scale corpus and labelling scheme for emotion recognition in task-oriented dialogue systems. In Proceedings of the 13th Language Resources and Evaluation Conference. 40964113.Google ScholarGoogle Scholar
  40. [40] Firdaus Mauajama, Ekbal Asif, and Bhattacharyya Pushpak. 2020. Incorporating politeness across languages in customer care responses: Towards building a multi-lingual empathetic dialogue agent. In Proceedings of the 12th Language Resources and Evaluation Conference. 41724182.Google ScholarGoogle Scholar
  41. [41] Firdaus Mauajama, Ekbal Asif, and Bhattacharyya Pushpak. 2022. PoliSe: Reinforcing politeness using user sentiment for customer care response generation. In Proceedings of the 29th International Conference on Computational Linguistics. 61656175.Google ScholarGoogle Scholar
  42. [42] Firdaus Mauajama, Priya Priyanshu, and Ekbal Asif. 2023. Mixing it up: Inducing empathy and politeness using multiple behaviour-aware generators for conversational systems. In Findings of the Association for Computational Linguistics (IJCNLP-AACL’23 Findings). 336347.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Firdaus Mauajama, Shandilya Arunav, Ekbal Asif, and Bhattacharyya Pushpak. 2022. Being polite: Modeling politeness variation in a personalized dialog agent. IEEE Trans. Comput. Soc. Syst. (2022).Google ScholarGoogle Scholar
  44. [44] Fischer John L.. 1965. The stylistic significance of consonantal sandhi in Trukese and Ponapean. Am. Anthropol. 67, 6 (1965), 14951502.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Fu Liye, Fussell Susan, and Danescu-Niculescu-Mizil Cristian. 2020. Facilitating the communication of politeness through fine-grained paraphrasing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 51275140.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] García Vicente, Sánchez José Salvador, and Mollineda Ramón Alberto. 2012. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl.-Bas. Syst. 25, 1 (2012), 1321.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. [47] Gino Eelen. 2001. A Critique of Politeness Theories. St Jerome, Manchester, UK.Google ScholarGoogle Scholar
  48. [48] Erving Goffman. 1967. Interaction ritual: Essays on face-to-face interaction (1st ed.). Routledge, New York, NY, USA.Google ScholarGoogle Scholar
  49. [49] Golchha Hitesh, Firdaus Mauajama, Ekbal Asif, and Bhattacharyya Pushpak. 2019. Courteously yours: Inducing courteous behavior in customer care responses using reinforced pointer generator network. 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). 851860.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Graham Sage Lambert. 2007. Disagreeing to agree: Conflict,(im) politeness and identity in a computer-mediated community. J. Pragmatics 39, 4 (2007), 742759.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Grice H. Paul. 1975. Logic and Conversation, Syntax and Semantics, vol. 3, P. Cole and J. Morgan (Eds.).Google ScholarGoogle Scholar
  52. [52] Gu Yueguo. 1990. Politeness phenomena in modern Chinese. J. Pragmatics 14, 2 (1990), 237257.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Gupta Swati, Walker Marilyn A., and Romano Daniela M.. 2007. How rude are you?: Evaluating politeness and affect in interaction. In International Conference on Affective Computing and Intelligent Interaction. Springer, 203217.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. [54] Haugh Michael. 2010. When is an email really offensive?: Argumentativity and variability in evaluations of impoliteness.Google ScholarGoogle Scholar
  55. [55] Francis Heylighen and Jean-Marc Dewaele. 1999. Formality of language: definition, measurement and behavioral determinants. Interner Bericht, Center “Leo Apostel”, Vrije Universiteit Brüssel, Center “Leo Apostel”, Free University of Brussels, Pleinlaan 2, B-1050 Brussels, Belgium.Google ScholarGoogle Scholar
  56. [56] Hoffman Erin R., McDonald David W., and Zachry Mark. 2017. Evaluating a computational approach to labeling politeness: Challenges for the application of machine classification to social computing data. Proc. ACM Hum.-Comput. Interact. 1, CSCW (2017), 114.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. [57] Holmes Janet. 1988. Paying compliments: A sex-preferential politeness strategy. J. Pragmatics 12, 4 (1988), 445465.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Holmes Janet. 2013. Women, Men and Politeness. Routledge.Google ScholarGoogle ScholarCross RefCross Ref
  59. [59] Holtgraves Thomas and Joong-Nam Yang. 1990. Politeness as universal: Cross-cultural perceptions of request strategies and inferences based on their use. J. Pers. Soc. Psychol. 59, 4 (1990), 719.Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Hovy Eduard. 1987. Generating natural language under pragmatic constraints. J. Pragmatics 11, 6 (1987), 689719.Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Hudson Richard. 1994. About 37% of word-tokens are nouns. Language 70, 2 (1994), 331339.Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Hutto Clayton and Gilbert Eric. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 8. 216225.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Inbar Ohad and Meyer Joachim. 2019. Politeness counts: Perceptions of peacekeeping robots. IEEE Trans. Hum.-Mach. Syst. 49, 3 (2019), 232240.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Irvine Judith T.. 1979. Formality and informality in communicative events. Am. Anthropol. 81, 4 (1979), 773790.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Janney Richard W. and Arndt Horst. 1993. Universality and relativity in cross-cultural politeness research: A historical perspective. Multilingua 12, 1 (1993), 13–50.Google ScholarGoogle Scholar
  66. [66] Jeong Martha, Minson Julia, Yeomans Michael, and Gino Francesca. 2019. Communicating with warmth in distributive negotiations is surprisingly counterproductive. Manage. Sci. 65, 12 (2019), 58135837.Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. [67] Johnson W. Lewis and Rizzo Paola. 2004. Politeness in tutoring dialogs:“run the factory, that’s what I’d do.” In International Conference on Intelligent Tutoring Systems. Springer, 6776.Google ScholarGoogle Scholar
  68. [68] Johnson W. Lewis, Rizzo Paola, Bosma Wauter, Kole Sander, Ghijsen Mattijs, and Welbergen Herwin van. 2004. Generating socially appropriate tutorial dialog. In Tutorial and Research Workshop on Affective Dialogue Systems. Springer, 254264.Google ScholarGoogle ScholarCross RefCross Ref
  69. [69] Joshi Chaitanya K., Mi Fei, and Faltings Boi. 2017. Personalization in goal-oriented dialog. arXiv:1706.07503. Retrieved from https://arxiv.org/abs/1706.07503Google ScholarGoogle Scholar
  70. [70] Kienpointner Manfred. 2008. Impoliteness and emotional arguments.Google ScholarGoogle Scholar
  71. [71] Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17), Palais des Congrès Neptune, Toulon, France. Retrieved from https://openreview.net/forum?id=SJU4ayYglGoogle ScholarGoogle Scholar
  72. [72] Klimt Bryan and Yang Yiming. 2004. Introducing the Enron corpus. In Proceedings of the Conference on Email and Anti-Spam (CEAS’04).Google ScholarGoogle Scholar
  73. [73] Klaus Krippendorff. 2007. Computing krippendorff.s alpha-reliability. http://www.asc.upenn.edu/Krippendorff/Google ScholarGoogle Scholar
  74. [74] Kumar Ritesh. 2012. Challenges in the development of annotated corpora of computer-mediated communication in Indian languages: A case of Hindi. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12). 299302.Google ScholarGoogle Scholar
  75. [75] Kumar Ritesh. 2014. Developing politeness annotated corpus of hindi blogs. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’14). 12751280.Google ScholarGoogle Scholar
  76. [76] Kumar Ritesh. 2021. Towards automatic identification of linguistic politeness in Hindi texts. arXiv:2111.15268. Retrieved from https://arxiv.org/abs/2111.15268Google ScholarGoogle Scholar
  77. [77] Kumar Ritesh and Jha Girish Nath. 2010. Translating politeness across cultures: Case of Hindi and English. In Proceedings of the 3rd International Conference on Intercultural Collaboration. 175178.Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. [78] Küntay Aylin C., Nakamura Keiko, and Şen B. Ateş. 2014. Crosslinguistic and crosscultural approaches to pragmatic development. In Pragmatic Development in First Language Acquisition, 317342.Google ScholarGoogle Scholar
  79. [79] Lakoff Robin. 1973. Language and woman’s place. Lang. Soc. 2, 1 (1973), 4579.Google ScholarGoogle ScholarCross RefCross Ref
  80. [80] Lakoff Robin. 1973. The logic of politeness: Or, minding your p’s and q’s. In Proceedings from the Annual Meeting of the Chicago Linguistic Society, Vol. 9. Chicago Linguistic Society, 292305.Google ScholarGoogle Scholar
  81. [81] Robin Tolmach Lakoff. 1989. The limits of politeness: Therapeutic and courtroom discourse in linguistic politeness II. Multilingua 8, 2.3 (1989), 101–129.Google ScholarGoogle Scholar
  82. [82] Langlotz Andreas and Locher Miriam A.. 2017. (Im) politeness and emotion. In The Palgrave Handbook of Linguistic (Im) Politeness. Springer, 287322.Google ScholarGoogle ScholarCross RefCross Ref
  83. [83] Leech G. N.. 1983. Principles of Pragmatics. Longman Group Ltd., London.Google ScholarGoogle Scholar
  84. [84] Leech Geoffrey. 1992. Pragmatic principles in Shaw’s you never can tell. Language, Text and Context: Essays in Stylistics (1992), 259–78.Google ScholarGoogle Scholar
  85. [85] Leech Geoffrey. 2005. Politeness: Is there an east-west divide. J. Foreign Lang. 6, 3 (2005), 130.Google ScholarGoogle Scholar
  86. [86] Lewis Mike, Liu Yinhan, Goyal Naman, Ghazvininejad Marjan, Mohamed Abdelrahman, Levy Omer, Stoyanov Veselin, and Zettlemoyer Luke. 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. 78717880.Google ScholarGoogle ScholarCross RefCross Ref
  87. [87] Li Can, Pang Bin, Wang Wenbo, Hu Lingshu, Gordon Matthew, Marinova Detelina, Balducci Bitty, and Shang Yi. 2023. How well can language models understand politeness? In Proceedings of the IEEE Conference on Artificial Intelligence (CAI’23). IEEE, 230231.Google ScholarGoogle ScholarCross RefCross Ref
  88. [88] Li Mingyang, Hickman Louis, Tay Louis, Ungar Lyle, and Guntuku Sharath Chandra. 2020. Studying politeness across cultures using english twitter and mandarin weibo. Proc. ACM Hum.-Comput. Interact. 4, CSCW2 (2020), 115.Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. [89] Li Xiujun, Wang Yu, Sun Siqi, Panda Sarah, Liu Jingjing, and Gao Jianfeng. 2018. Microsoft dialogue challenge: Building end-to-end task-completion dialogue systems. arXiv:1807.11125. Retrieved from https://arxiv.org/abs/1807.11125Google ScholarGoogle Scholar
  90. [90] Li Yanran, Su Hui, Shen Xiaoyu, Li Wenjie, Cao Ziqiang, and Niu Shuzi. 2017. DailyDialog: A manually labelled multi-turn dialogue dataset. In Proceedings of the 8th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 986995.Google ScholarGoogle Scholar
  91. [91] Liévin Valentin, Hother Christoffer Egeberg, and Winther Ole. 2022. Can large language models reason about medical questions? arXiv:2207.08143. Retrieved from https://arxiv.org/abs/2207.08143Google ScholarGoogle Scholar
  92. [92] Lin Chin-Yew. 2004. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Out. 7481.Google ScholarGoogle Scholar
  93. [93] Lin Yen-Ting and Chen Yun-Nung. 2023. LLM-Eval: Unified multi-dimensional automatic evaluation for open-domain conversations with large language models. In Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI’23), Chen Yun-Nung and Rastogi Abhinav (Eds.). Association for Computational Linguistics, 4758. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  94. [94] Miriam A Locher. 2006. Polite behavior within relational work: The discursive approach to politeness. Multilingua 25, 3 (2006), 249–267.Google ScholarGoogle Scholar
  95. [95] Locher Miriam A. and Glick D. J.. 2006. Power and politeness in action: Disagreements in oral communication. Lang. Soc. Lond. 35, 5 (2006), 729732.Google ScholarGoogle ScholarCross RefCross Ref
  96. [96] Locher Miriam A. and Watts Richard J.. 2005. Politeness theory and relational work.Google ScholarGoogle Scholar
  97. [97] Locher Miriam A. and Watts Richard J.. 2008. Relational work and impoliteness: Negotiating norms of linguistic behaviour. Lang. Power Soc. Process 21 (2008), 77.Google ScholarGoogle Scholar
  98. [98] Madaan Aman, Setlur Amrith, Parekh Tanmay, Poczós Barnabás, Neubig Graham, Yang Yiming, Salakhutdinov Ruslan, Black Alan W., and Prabhumoye Shrimai. 2020. Politeness transfer: A tag and generate approach. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 18691881.Google ScholarGoogle ScholarCross RefCross Ref
  99. [99] Maratsos Michael P.. 1973. Nonegocentric communication abilities in preschool children. Child Dev. (1973), 697700.Google ScholarGoogle ScholarCross RefCross Ref
  100. [100] McElhinny Bonnie. 1996. Language and gender. Sociolinguist. Lang. Teach. (1996), 218.Google ScholarGoogle Scholar
  101. [101] McHugh Mary L.. 2012. Interrater reliability: The kappa statistic. Biochem. Med. 22, 3 (2012), 276282.Google ScholarGoogle ScholarCross RefCross Ref
  102. [102] Miller Christopher, Wu Peggy, Funk H., Johnson Lewis, and Viljalmsson H.. 2007. A computational approach to etiquette and politeness: An “Etiquette Engine™” for cultural interaction training. In Conference on Behavior Representation in Modeling and Simulation (BRIMS’07).Google ScholarGoogle Scholar
  103. [103] Miller Christopher, Wu Peggy, Funk H., Wilson Peggy, and Johnson Lewis. 2006. A computational approach to etiquette and politeness: Initial test cases. In Conference on Behavior Representation in Modeling and Simulation (BRIMS’06). 1518.Google ScholarGoogle Scholar
  104. [104] Miller Christopher A., Wu Peggy, and Funk Harry B.. 2008. A computational approach to etiquette: Operationalizing Brown and Levinson’s politeness model. IEEE Intell. Syst. 23, 4 (2008), 2835.Google ScholarGoogle ScholarDigital LibraryDigital Library
  105. [105] Mills Sara. 2003. Gender and Politeness. Number 17. Cambridge University Press.Google ScholarGoogle ScholarCross RefCross Ref
  106. [106] Minson Julia A., Chen Frances S., and Tinsley Catherine H.. 2020. Why won’t you listen to me? Measuring receptiveness to opposing views. Manage. Sci. 66, 7 (2020), 30693094.Google ScholarGoogle ScholarDigital LibraryDigital Library
  107. [107] Mishra Kshitij, Firdaus Mauajama, and Ekbal Asif. 2022. Please be polite: Towards building a politeness adaptive dialogue system for goal-oriented conversations. Neurocomputing 494 (2022), 242254.Google ScholarGoogle ScholarCross RefCross Ref
  108. [108] Mishra Kshitij, Firdaus Mauajama, and Ekbal Asif. 2022. Predicting politeness variations in goal-oriented conversations. IEEE Trans. Comput. Soc. Syst. (2022).Google ScholarGoogle Scholar
  109. [109] Mishra Kshitij, Firdaus Mauajama, and Ekbal Asif. 2023. GenPADS: Reinforcing politeness in an end-to-end dialogue system. PLoS One 18, 1 (2023), e0278323.Google ScholarGoogle ScholarCross RefCross Ref
  110. [110] Mishra Kshitij, Priya Priyanshu, Burja Manisha, and Ekbal Asif. 2023. e-THERAPIST: I suggest you to cultivate a mindset of positivity and nurture uplifting thoughts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1395213967.Google ScholarGoogle ScholarCross RefCross Ref
  111. [111] Mishra Kshitij, Priya Priyanshu, and Ekbal Asif. 2023. Help me heal: A reinforced polite and empathetic mental health and legal counseling dialogue system for crime victims. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 1440814416.Google ScholarGoogle ScholarDigital LibraryDigital Library
  112. [112] Mishra Kshitij, Priya Priyanshu, and Ekbal Asif. 2023. PAL to lend a helping hand: Towards building an emotion adaptive polite and empathetic counseling conversational agent. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1225412271.Google ScholarGoogle ScholarCross RefCross Ref
  113. [113] Mishra Kshitij, Samad Azlaan Mustafa, Totala Palak, and Ekbal Asif. 2022. PEPDS: A polite and empathetic persuasive dialogue system for charity donation. In Proceedings of the 29th International Conference on Computational Linguistics. 424440.Google ScholarGoogle Scholar
  114. [114] Miyamoto Tomoki, Katagami Daisuke, and Usami Mayumi. 2020. A politeness control method for conversational agents considering social relationships with users. In Annual Conference of the Japanese Society for Artificial Intelligence. Springer, 224231.Google ScholarGoogle Scholar
  115. [115] Mohammad Saif M. and Turney Peter D.. 2013. Crowdsourcing a word–emotion association lexicon. Comput. Intell. 29, 3 (2013), 436465.Google ScholarGoogle ScholarCross RefCross Ref
  116. [116] Montgomery Michael B.. 1998. Multiple modals in LAGS. In From the Gulf States and Beyond: The Legacy of Lee Pederson and LAGS, 90.Google ScholarGoogle Scholar
  117. [117] Mukherjee Sourabrata, Hudeček Vojtěch, and Dušek Ondřej. 2023. Polite Chatbot: A text style transfer application. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop. 8793.Google ScholarGoogle Scholar
  118. [118] Munkova Dasa, Munk Michal, and Fráterová Zuzana. 2013. Identifying social and expressive factors in request texts using transaction/sequence model. In Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP’13). 496503.Google ScholarGoogle Scholar
  119. [119] Nakamura Keiko, Shirai Y., Kobayashi S., and Miyata S.. 2002. Polite language usage in mother-infant interactions: A look at language socialization. Stud. Lang. Sci. 2 (2002), 175191.Google ScholarGoogle Scholar
  120. [120] Napoles Courtney, Pappu Aasish, and Tetreault Joel. 2017. Automatically identifying good conversations online (yes, they do exist!). In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 11. 628631.Google ScholarGoogle ScholarCross RefCross Ref
  121. [121] Napoles Courtney, Tetreault Joel, Pappu Aasish, Rosato Enrica, and Provenzale Brian. 2017. Finding good conversations online: The yahoo news annotated comments corpus. In Proceedings of the 11th Linguistic Annotation Workshop. 1323.Google ScholarGoogle ScholarCross RefCross Ref
  122. [122] Niu Tong and Bansal Mohit. 2018. Polite dialogue generation without parallel data. Trans. Assoc. Comput. Ling. 6 (2018), 373389.Google ScholarGoogle ScholarCross RefCross Ref
  123. [123] Ortony Andrew, Clore Gerald L., and Collins Allan. 1988. The cognitive structure of emotions. Cambridge University Press, Cambridge, UK.Google ScholarGoogle Scholar
  124. [124] Papineni Kishore, Roukos Salim, Ward Todd, and Zhu Wei-Jing. 2002. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 311318.Google ScholarGoogle ScholarDigital LibraryDigital Library
  125. [125] James W Pennebaker, Martha E Francis, and Roger J Booth. 2001. Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates 71, 2001 (2001), 2001.Google ScholarGoogle Scholar
  126. [126] Pérez-Rosas Verónica, Wu Xinyi, Resnicow Kenneth, and Mihalcea Rada. 2019. What makes a good counselor? learning to distinguish between high-quality and low-quality counseling conversations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 926935.Google ScholarGoogle ScholarCross RefCross Ref
  127. [127] Peskov Denis, Clarke Nancy, Krone Jason, Fodor Brigi, Zhang Yi, Youssef Adel, and Diab Mona. 2019. Multi-domain goal-oriented dialogues (multidogo): Strategies toward curating and annotating large scale dialogue data. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 45264536.Google ScholarGoogle ScholarCross RefCross Ref
  128. [128] Pfuderer C.. 1968. A scale of politeness of request forms in English. Term paper for Speech 164A. University of California, Berkeley.Google ScholarGoogle Scholar
  129. [129] Priya Priyanshu, Firdaus Mauajama, and Ekbal Asif. 2023. A multi-task learning framework for politeness and emotion detection in dialogues for mental health counselling and legal aid. Expert Syst. Appl. 224 (2023), 120025.Google ScholarGoogle ScholarDigital LibraryDigital Library
  130. [130] Priya Priyanshu, Mishra Kshitij, Totala Palak, and Ekbal Asif. 2023. PARTNER: A persuasive mental health and legal counselling dialogue system for women and children crime victims. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI’23), Elkind Edith (Ed.), 61836191. DOI:AI for Good.Google ScholarGoogle ScholarDigital LibraryDigital Library
  131. [131] Pu Dongqi and Demberg Vera. 2023. ChatGPT vs human-authored text: Insights into controllable text summarization and sentence style transfer. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), Padmakumar Vishakh, Vallejo Gisela, and Fu Yao (Eds.). Association for Computational Linguistics, 118. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  132. [132] Qian Jing, Bethke Anna, Liu Yinyin, Belding Elizabeth, and Wang William Yang. 2019. A benchmark dataset for learning to intervene in online hate speech. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 47554764.Google ScholarGoogle ScholarCross RefCross Ref
  133. [133] Radford Alec, Wu Jeffrey, Child Rewon, Luan David, Amodei Dario, Sutskever Ilya, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.Google ScholarGoogle Scholar
  134. [134] Ramamurthy Rajkumar, Ammanabrolu Prithviraj, Brantley Kianté, Hessel Jack, Sifa Rafet, Bauckhage Christian, Hajishirzi Hannaneh, and Choi Yejin. 2022. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv:2210.01241. https://arxiv.org/abs/2210.01241Google ScholarGoogle Scholar
  135. [135] Rana Kanishk, Madaan Rahul, and Shukla Jainendra. 2021. Effect of polite triggers in chatbot conversations on user experience across gender, age, and personality. In Proceedings of the 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN’21). IEEE, 813819.Google ScholarGoogle ScholarDigital LibraryDigital Library
  136. [136] Bernd Renner. 2020. (In) directness as an (im) politeness strategy in the contact between german and Brazilian portuguese as additional languages. phdthesis. Universidade de Brasília, Brasília.Google ScholarGoogle Scholar
  137. [137] Roller Stephen, Dinan Emily, Goyal Naman, Ju Da, Williamson Mary, Liu Yinhan, Xu Jing, Ott Myle, Smith Eric Michael, Boureau Y-Lan, et al. 2021. Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 300325.Google ScholarGoogle ScholarCross RefCross Ref
  138. [138] Roman Norton Trevisan, Piwek Paul, and Carvalho A. M. B. R.. 2004. Politeness and summarization: An exploratory study. In Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications (AAAI-EAAT’04).Google ScholarGoogle Scholar
  139. [139] Roman Norton Trevisan, Piwek Paul, and Carvalho Ariadne Maria Brito Rizzoni. 2006. Politeness and bias in dialogue summarization: Two exploratory studies. In Computing Attitude and Affect in Text: Theory and Applications. Springer, 171185.Google ScholarGoogle ScholarCross RefCross Ref
  140. [140] Saha Punyajoy, Singh Kanishk, Kumar Adarsh, Mathew Binny, and Mukherjee Animesh. 2022. CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI’22), Raedt Lud De (Ed.), 51575163. DOI:AI for Good.Google ScholarGoogle ScholarCross RefCross Ref
  141. [141] Sanh Victor, Debut Lysandre, Chaumond Julien, and Wolf Thomas. 2019. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. In NeurIPS EMC\(^2\) Workshop.Google ScholarGoogle Scholar
  142. [142] Schulman John, Wolski Filip, Dhariwal Prafulla, Radford Alec, and Klimov Oleg. 2017. Proximal policy optimization algorithms. arXiv:1707.06347. Retrieved from https://arxiv.org/abs/1707.06347Google ScholarGoogle Scholar
  143. [143] Searle John R. and Searle John Rogers. 1969. Speech Acts: An Essay in the Philosophy of Language. Vol. 626. Cambridge University Press.Google ScholarGoogle ScholarCross RefCross Ref
  144. [144] Sennrich Rico, Haddow Barry, and Birch Alexandra. 2016. Controlling politeness in neural machine translation via side constraints. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3540.Google ScholarGoogle ScholarCross RefCross Ref
  145. [145] Sennrich Rico, Volk Martin, and Schneider Gerold. 2013. Exploiting synergies between open resources for german dependency parsing, pos-tagging, and morphological analysis. In Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP’13). 601609.Google ScholarGoogle Scholar
  146. [146] Sharma Ashish, Miner Adam, Atkins David, and Althoff Tim. 2020. A computational approach to understanding empathy expressed in text-based mental health support. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 52635276.Google ScholarGoogle ScholarCross RefCross Ref
  147. [147] Shatz Marilyn and Gelman Rochel. 1973. The development of communication skills: Modifications in the speech of young children as a function of listener. Monogr. Soc. Res. Child Dev. (1973), 138.Google ScholarGoogle ScholarCross RefCross Ref
  148. [148] Silva Diogo, Semedo David, and Magalhães João. 2022. Polite task-oriented dialog agents: To generate or to rewrite?. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis. 304314.Google ScholarGoogle ScholarCross RefCross Ref
  149. [149] Singh Gopendra Vikram, Priya Priyanshu, Firdaus Mauajama, Ekbal Asif, and Bhattacharyya Pushpak. 2022. EmoInHindi: A multi-label emotion and intensity annotated dataset in hindi for emotion recognition in dialogues. In Proceedings of the 13th Language Resources and Evaluation Conference. 58295837.Google ScholarGoogle Scholar
  150. [150] Song Mengmeng, Zhang Huixian, Xing Xinyu, and Duan Yucong. 2023. Appreciation vs. apology: Research on the influence mechanism of chatbot service recovery based on politeness theory. J. Retail. Consum. Serv. 73 (2023), 103323.Google ScholarGoogle ScholarCross RefCross Ref
  151. [151] Sperlich Darcy, Leem Jaiho, and Ahn Eui-Jeen. 2016. The interaction of politeness systems in Korean learners of French. In Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers. 163171.Google ScholarGoogle Scholar
  152. [152] Srinivasan Anirudh and Choi Eunsol. 2022. TyDiP: A dataset for politeness classification in nine typologically diverse languages. In Findings of the Association for Computational Linguistics: EMNLP’22. 57235738.Google ScholarGoogle ScholarCross RefCross Ref
  153. [153] Sweetman David and Luthans Fred. 2010. The power of positive psychology: Psychological capital and work engagement. In Work Engagement: A Handbook of Essential Theory and Research, Vol. 54, 68.Google ScholarGoogle Scholar
  154. [154] Terkourafi Mariana. 2001. Politeness in Cypriot Greek: A Frame-based Approach. Ph. D. Dissertation. Citeseer.Google ScholarGoogle Scholar
  155. [155] Terkourafi Marina. 2002. Politeness and formulaicity: Evidence from Cypriot Greek. J. Greek Ling. 3, 1 (2002), 179201.Google ScholarGoogle ScholarCross RefCross Ref
  156. [156] Marina Terkourafi. 2005. Beyond the micro-level in politeness research. Journal of Politeness Research 1, (2005), 237262.Google ScholarGoogle Scholar
  157. [157] Terkourafi Marina. 2005. Pragmatic correlates of frequency of use: The case for a notion of “minimal context.” Trends Ling. Stud. Monogr. 161 (2005), 209.Google ScholarGoogle Scholar
  158. [158] Thomas Jenny A.. 2014. Meaning in Interaction: An Introduction to Pragmatics. Routledge.Google ScholarGoogle ScholarCross RefCross Ref
  159. [159] Tiedemann Jörg. 2012. Parallel data, tools and interfaces in OPUS. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’12), Vol. 2012. Citeseer, 22142218.Google ScholarGoogle Scholar
  160. [160] Touvron Hugo, Lavril Thibaut, Izacard Gautier, Martinet Xavier, Lachaux Marie-Anne, Lacroix Timothée, Rozière Baptiste, Goyal Naman, Hambro Eric, Azhar Faisal, et al. 2023. Llama: Open and efficient foundation language models. arXiv:2302.13971. Retrieved from https://arxiv.org/abs/2302.13971Google ScholarGoogle Scholar
  161. [161] Vaswani Ashish, Shazeer Noam, Parmar Niki, Uszkoreit Jakob, Jones Llion, Gomez Aidan N., Kaiser Łukasz, and Polosukhin Illia. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 59986008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  162. [162] Viswanathan Aditi, Wang Varden, and Kononova Antonina. 2019. Controlling formality and style of machine translation output using AutoML. In Annual International Symposium on Information Management and Big Data. Springer, 306313.Google ScholarGoogle Scholar
  163. [163] Vogel Carl. 2015. Some puzzles of politeness and impoliteness within a formal semantics of offensive language. In Conflict and Multimodal Communication: Social Research and Machine Intelligence, 223241.Google ScholarGoogle ScholarCross RefCross Ref
  164. [164] Voigt Rob, Camp Nicholas P., Prabhakaran Vinodkumar, Hamilton William L., Hetey Rebecca C., Griffiths Camilla M., Jurgens David, Jurafsky Dan, and Eberhardt Jennifer L.. 2017. Language from police body camera footage shows racial disparities in officer respect. Proc. Natl. Acad. Sci. U.S.A. 114, 25 (2017), 65216526.Google ScholarGoogle ScholarCross RefCross Ref
  165. [165] Walker Marilyn A., Cahn Janet E., and Whittaker Stephen J.. 1997. Improvising linguistic style: Social and affective bases for agent personality. In Proceedings of the 1st International Conference on Autonomous Agents. 96105.Google ScholarGoogle ScholarDigital LibraryDigital Library
  166. [166] Wang Xuewei, Shi Weiyan, Kim Richard, Oh Yoojung, Yang Sijia, Zhang Jingwen, and Yu Zhou. 2019. Persuasion for good: Towards a personalized persuasive dialogue system for social good. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 56355649.Google ScholarGoogle ScholarCross RefCross Ref
  167. [167] Wang Yi-Chia, Papangelis Alexandros, Wang Runze, Feizollahi Zhaleh, Tur Gokhan, and Kraut Robert. 2020. Can you be more social? Injecting politeness and positivity into task-oriented conversational agents. arXiv:2012.14653. Retrieved from https://arxiv.org/abs/2012.14653Google ScholarGoogle Scholar
  168. [168] Wardhaugh Ronald. 2006. An Introduction to Sociolinguistics.Google ScholarGoogle Scholar
  169. [169] Waseem Zeerak, Davidson Thomas, Warmsley Dana, and Weber Ingmar. 2017. Understanding abuse: A typology of abusive language detection subtasks. In Proceedings of the 1st Workshop on Abusive Language Online. 7884.Google ScholarGoogle ScholarCross RefCross Ref
  170. [170] Watts Richard J.. 2003. Politeness. Cambridge University Press.Google ScholarGoogle ScholarCross RefCross Ref
  171. [171] Watts Richard J.. 2005. 2. Linguistic politeness and politic verbal behaviour: Reconsidering claims for universality. In Politeness in Language Studies in its History, Theory and Practice, 4369.Google ScholarGoogle ScholarCross RefCross Ref
  172. [172] WHO World Health Organization. 2022. Mental Disorders. Retrieved from https://www.who.int/news-room/fact-sheets/detail/mental-disordersGoogle ScholarGoogle Scholar
  173. [173] Williams Jason D., Henderson Matthew, Raux Antoine, Thomson Blaise, Black Alan, and Ramachandran Deepak. 2014. The dialog state tracking challenge series. AI Mag. 35, 4 (2014), 121124.Google ScholarGoogle ScholarDigital LibraryDigital Library
  174. [174] Yeomans Michael, Kantor Alejandro, and Tingley Dustin. 2018. The politeness package: Detecting politeness in natural language. R J. 10, 2 (2018).Google ScholarGoogle ScholarCross RefCross Ref
  175. [175] Yeomans Michael, Minson Julia, Collins Hanne, Chen Frances, and Gino Francesca. 2020. Conversational receptiveness: Improving engagement with opposing views. Org. Behav. Hum. Decis. Process. 160 (2020), 131148.Google ScholarGoogle ScholarCross RefCross Ref
  176. [176] Yu Yong, Si Xiaosheng, Hu Changhua, and Zhang Jianxun. 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31, 7 (2019), 12351270.Google ScholarGoogle ScholarDigital LibraryDigital Library
  177. [177] Yule George. 2020. The Study of Language. Cambridge University Press.Google ScholarGoogle Scholar
  178. [178] Zhang Justine, Chang Jonathan, Danescu-Niculescu-Mizil Cristian, Dixon Lucas, Hua Yiqing, Taraborelli Dario, and Thain Nithum. 2018. Conversations gone awry: Detecting early signs of conversational failure. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 13501361.Google ScholarGoogle ScholarCross RefCross Ref
  179. [179] Zhang Yizhe, Sun Siqi, Galley Michel, Chen Yen-Chun, Brockett Chris, Gao Xiang, Gao Jianfeng, Liu Jingjing, and Dolan William B.. 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. 270278.Google ScholarGoogle ScholarCross RefCross Ref
  180. [180] Zhao Weixiang, Zhao Yanyan, Lu Xin, Wang Shilong, Tong Yanpeng, and Qin Bing. 2023. Is ChatGPT equipped with emotional dialogue capabilities? arXiv:2304.09582. Retrieved from https://arxiv.org/abs/2304.09582Google ScholarGoogle Scholar
  181. [181] Zhao Wayne Xin, Zhou Kun, Li Junyi, Tang Tianyi, Wang Xiaolei, Hou Yupeng, Min Yingqian, Zhang Beichen, Zhang Junjie, Dong Zican, et al. 2023. A survey of large language models. arXiv:2303.18223. Retrieved from https://arxiv.org/abs/2303.18223Google ScholarGoogle Scholar
  182. [182] Zhou Naitian and Jurgens David. 2020. Condolence and empathy in online communities. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 609626.Google ScholarGoogle ScholarCross RefCross Ref
  183. [183] Ziems Caleb, Held William, Shaikh Omar, Chen Jiaao, Zhang Zhehao, and Yang Diyi. 2023. Can large language models transform computational social science? arXiv:2305.03514. Retrieved from https://arxiv.org/abs/2305.03514Google ScholarGoogle Scholar

Index Terms

  1. Computational Politeness in Natural Language Processing: A Survey

      Recommendations

      Comments

      Login options

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

      Sign in

      Full Access

      • Published in

        cover image ACM Computing Surveys
        ACM Computing Surveys  Volume 56, Issue 9
        September 2024
        980 pages
        ISSN:0360-0300
        EISSN:1557-7341
        DOI:10.1145/3613649
        • Editors:
        • David Atienza,
        • Michela Milano
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 8 May 2024
        • Online AM: 2 April 2024
        • Accepted: 10 March 2024
        • Revised: 27 February 2024
        • Received: 4 August 2022
        Published in csur Volume 56, Issue 9

        Check for updates

        Qualifiers

        • survey
      • Article Metrics

        • Downloads (Last 12 months)354
        • Downloads (Last 6 weeks)182

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Full Text

      View this article in Full Text.

      View Full Text