Elamur Research
☰
home / papers / 2021 / The Importance of Modeling Social Factors of Language: Theory and Practice

The Importance of Modeling Social Factors of Language: Theory and Practice

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2021.naacl-main.49.pdf

Summary

This position paper argues that NLP has hit a ceiling because it treats language mainly as a carrier of information and largely ignores who is speaking, to whom, in what setting, and for what purpose. The authors compare this to economics before behavioral economics, when rational-actor assumptions simplified human behavior. The paper presents no new dataset or model. Instead, it builds a conceptual framework from two linguistic theories: systemic functional linguistics (SFL) and Grice's Cooperative Principle. Grice's principle explains why deliberately breaking conversational maxims produces implied meanings such as sarcasm. From these theories, the authors derive a taxonomy of seven social factors. For each factor, they review where current systems fail and where early socially aware methods already bring gains. They conclude that modeling social factors could improve existing systems, enable new applications, and make tools fairer. They also flag open problems: data needs, privacy and ethics, evaluation metrics, and multimodal interaction.

Key points

  • The taxonomy's seven factors cover the speaker and the receiver, the relationship between them, the surrounding context, the norms a society holds, cultural and ideological background, and the goals of communication. The authors note that these categories overlap.
  • The theoretical basis is SFL (Halliday and Matthiessen, 2013) and Grice's (1975) four maxims: Quality, Quantity, Relevance, and Manner. Conversational implicature is used to explain why NLP struggles with sarcasm detection and entailment.
  • Figure 1 tracks ACL acceptances over about ten years in three tracks: computational social science/social media, sentiment analysis, and discourse/pragmatics. The counts show growing interest in social factors.
  • Example failures include machine translation that makes a young German woman's text read like an elderly American man's (Hovy et al., 2020). Another example is dialogue agents whose personality is inconsistent because they are trained on many speakers' conversations.
  • Open challenges include collecting representative social annotations, protecting privacy and preventing de-anonymization, designing metrics beyond task performance, and extending the taxonomy to multimodal models.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Implicature

Cited by

112 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 106 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.naacl-main.49

@inproceedings{hovy2021importance,
  title = {{The Importance of Modeling Social Factors of Language: Theory and Practice}},
  author = {Hovy, Dirk and Yang, Diyi},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.49},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.49}
}

References

114 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Gavin Abercrombie and Dirk Hovy. 2016. Putting sarcasm detection into context: The effects of class imbalance and manual labelling on supervised machine classification of Twitter conversations. In Proceedings of the ACL 2016 Student Research Workshop, pages 107–113, Berlin, Germany. Association for… find
  2. Nicolas Anastassacos, Stephen Hailes, and Mirco Musolesi. 2020. Partner selection for the emergence of cooperation in multi-agent systems using reinforcement learning. In AAAI, pages 7047–7054. find
  3. Rafael E Banchs and Haizhou Li. 2012. Iris: a chat-oriented dialogue system based on the vector space model. In Proceedings of the ACL 2012 System Demonstrations, pages 37–42. Association for Computational Linguistics. find
  4. Justin Basilico and Thomas Hofmann. 2004. Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning, page 9. find
  5. Emily M Bender and Batya Friedman. 2018. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics, 6:587–604. find
  6. Emily M. Bender and Alexander Koller. 2020. Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5185–5198, Online. Association for Computational Linguistics. find our card
  7. Adrian Benton, Margaret Mitchell, and Dirk Hovy. 2017. Multitask learning for mental health conditions with limited social media data. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 152–162, Valencia, Spain… find
  8. Basil Bernstein. 1960. Language and social class. The British journal of sociology, 11(3):271–276. find
  9. Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, and Joseph Turian. 2020. Experience grounds language. In Proceedings of the 2020 Conference on Empirical Methods in Natural Lang… find our card
  10. Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. Language (technology) is power: A critical survey of “bias” in NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5454– 5476, Online. Association for Computational Linguistics. find our card
  11. Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016. Demographic dialectal variation in social media: A case study of African-American English. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1119–1130, Austin, Texas. Association for Computational… find our card
  12. Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165. find
  13. Ozan Caglayan, Pranava Swaroop Madhyastha, Lucia Specia, and Loïc Barrault. 2019. Probing the need for visual context in multimodal machine translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… find
  14. Amanda Cercas Curry and Verena Rieser. 2018. \#MeToo Alexa: How conversational systems respond to sexual harassment. In Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing, pages 7–14, New Orleans, Louisiana, USA. Association for Computational Linguistics. find
  15. Amanda Cercas Curry, Judy Robertson, and Verena Rieser. 2020. Conversational assistants and gender stereotypes: Public perceptions and desiderata for voice personas. In Proceedings of the Second Workshop on Gender Bias in Natural Language Processing, pages 72–78, Barcelona, Spain (Online). Associati… find
  16. Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, and Eric Gilbert. 2018. The internet’s hidden rules: An empirical study of reddit norm violations at micro, meso, and macro scales. Proceedings of the ACM on Human-Computer Interaction,… find
  17. Jiaao Chen and Diyi Yang. 2021. Weakly-supervised hierarchical models for predicting persuasive strategies in good-faith textual requests. AAAI. find
  18. Herbert H. Clark and Michael F. Schober. 1992. Asking questions and influencing answers. Questions about Questions: Inquiries into the Cognitive Bases of Surveys, pages 15–48. find
  19. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of machine learning research, 12(ARTICLE):2493–2537. find our card
  20. Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013a. 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), p… find our card
  21. Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013b. No country for old members: User lifecycle and linguistic change in online communities. In Proceedings of the 22nd international conference on World Wide Web, pages 307–318. find
  22. Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde, and Raquel Fernández. 2019. You shall know a user by the company it keeps: Dynamic representations for social media users in nlp. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inte… find
  23. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,… find
  24. Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams. 2020. Multidimensional gender bias classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 314–331, Online. Association for Computational Linguistics. find
  25. Rachel Dorn. 2019. Dialect-specific models for automatic speech recognition of African American Vernacular English. In Proceedings of the Student Research Workshop Associated with RANLP 2019, pages 16–20, Varna, Bulgaria. INCOMA Ltd. find
  26. Penelope Eckert. 2012. Three waves of variation study: The emergence of meaning in the study of sociolinguistic variation. Annual review of Anthropology, 41:87–100. find
  27. Svea Eckert and Andreas Dewes. 2017. Dark data. Presentation at DEFCON, 25. find
  28. Marcello Federico. 1996. Bayesian estimation methods for n-gram language model adaptation. In Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP’96, volume 1, pages 240–243. IEEE. find
  29. Ernst Fehr and Urs Fischbacher. 2004. Social norms and human cooperation. Trends in cognitive sciences, 8(4):185–190. find
  30. Charles J Fillmore. 1982. Frame semantics. In Linguistics in the morning calm, pages 111–137. find
  31. Lucie Flek. 2020. Returning the N to NLP: Towards contextually personalized classification models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7828– 7838, Online. Association for Computational Linguistics. find
  32. Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. 2020. Social chemistry 101: Learning to reason about social and moral norms. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 653–670, Online. Association for Computatio… find
  33. Tommaso Fornaciari and Dirk Hovy. 2019. Dense node representation for geolocation. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 224–230, Hong Kong, China. Association for Computational Linguistics. find
  34. Liye Fu, Susan Fussell, and Cristian Danescu-Niculescu-Mizil. 2020. Facilitating the communication of politeness through fine-grained paraphrasing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5127–5140, Online. Association for Computationa… find our card
  35. Fengping Gao. 2005. Japanese: A heavily culture-laden language. Journal of Intercultural Communication, 10:1404–1634. find
  36. Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16):E3635–E3644. find
  37. Malcolm Gladwell. 2008. Outliers: The story of success. Little, Brown. find
  38. Hila Gonen and Yoav Goldberg. 2019. Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol… find our card
  39. Herbert P Grice. 1975. Logic and conversation. In Speech acts, pages 41–58. Brill. find
  40. Michael Alexander Kirkwood Halliday and Christian MIM Matthiessen. 2013. Halliday’s introduction to functional grammar. Routledge. find
  41. Dirk Hovy. 2015. Demographic factors improve classification performance. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 752–762, Beijing, China. Associ… find
  42. Dirk Hovy. 2016. The enemy in your own camp: How well can we detect statistically-generated fake reviews – an adversarial study. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 351–356, Berlin, Germany. Association for Comput… find
  43. Dirk Hovy. 2018. The social and the neural network: How to make natural language processing about people again. In Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pages 42–49, New Orleans, Louisiana, USA. Association for C… find
  44. Dirk Hovy, Federico Bianchi, and Tommaso Fornaciari. 2020. “You sound just like your father” Commercial machine translation systems include stylistic biases. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1686–1690, Online. Association for Computati… find
  45. Dirk Hovy and Christoph Purschke. 2018. Capturing regional variation with distributed place representations and geographic retrofitting. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4383–4394, Brussels, Belgium. Association for Computational Lingui… find
  46. Dirk Hovy and Shannon L. Spruit. 2016. The social impact of natural language processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 591–598, Berlin, Germany. Association for Computational Linguistics. find our card
  47. Eduard Hovy. 1987. Generating natural language under pragmatic constraints. Journal of Pragmatics, 11(6):689–719. find
  48. Dell Hymes. 1972. On communicative competence. sociolinguistics, 269293:269–293. find
  49. Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daumé III. 2016. Feuding families and former friends: Unsupervised learning for dynamic fictional relationships. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguist… find
  50. Dániel Z Kádár and Sara Mills. 2011. Politeness in East Asia. Cambridge University Press. find
  51. Mohammad Hossein Keshavarz. 2001. The role of social context, intimacy, and distance in the choice of forms of address. International journal of the sociology of language, 2001(148):5–18. find
  52. Sung-wan Kim and HyoJung Lee. 2017. A study on machine translation outputs: Korean to english translation of embedded sentences. 22(4):123–147. find
  53. Austin C Kozlowski, Matt Taddy, and James A Evans. 2018. The geometry of culture: Analyzing meaning through word embeddings. arXiv preprint arXiv:1803.09288. — [arXiv:1803.09288](https://arxiv.org/abs/1803.09288) find
  54. Vinodh Krishnan and Jacob Eisenstein. 2015. “You’re mr. Lebowski, I’m the Dude”: Inducing address term formality in signed social networks. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1616–16… find
  55. William Labov. 1972. Sociolinguistic patterns. University of Pennsylvania Press. find
  56. Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. 2019. Disentangled representation learning for non-parallel text style transfer. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 424–434, Florence, Italy. Association for Computational Li… find our card
  57. Angeliki Lazaridou, Nghia The Pham, and Marco Baroni. 2015. Combining language and vision with a multimodal skip-gram model. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 153–163, Denver, Color… find
  58. Ruth Jones and Ann Irvine. 2013. The (un)faithful machine translator. In Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pages 96–101, Sofia, Bulgaria. Association for Computational Linguistics. find
  59. 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), pages 994–1003. find our card
  60. Anna Jørgensen, Dirk Hovy, and Anders Søgaard. 2016. Learning a POS tagger for AAVE-like language. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1115–1120, San Diego, California. Association fo… find our card
  61. Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020. The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6282–6293, Online. Association for Comp… find our card
  62. Prathyusha Jwalapuram. 2017. Evaluating dialogs based on Grice’s maxims. In Proceedings of the Student Research Workshop Associated with RANLP 2017, pages 17–24, Varna. INCOMA Ltd. find
  63. Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018. Towards robust and privacy-preserving text representations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 25–30, Melbourne, Australia. Association for Computational Linguis… find
  64. Eden Litt. 2012. Knock, knock. who’s there? the imagined audience. Journal of broadcasting & electronic media, 56(3):330–345. find
  65. Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, and H. Andrew Schwartz. 2017. Human centered NLP with user-factor adaptation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1146–1155, Copenhagen, Denmark. Association for Comput… find
  66. Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye. 2020. Politeness transfer: A tag and generate approach. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, page… find our card
  67. Christopher D Manning. 2015. Computational linguistics and deep learning. Computational Linguistics, 41(4):701–707. find
  68. Evgeny Matusov. 2019. The challenges of using neural machine translation for literature. In Proceedings of the Qualities of Literary Machine Translation, pages 10–19, Dublin, Ireland. European Association for Machine Translation. find
  69. Shachar Mirkin and Jean-Luc Meunier. 2015. Personalized machine translation: Predicting translational preferences. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2019–2025. find
  70. Dong Nguyen, A. Seza Doğruöz, Carolyn P. Rosé, and Franciska de Jong. 2016. Survey: Computational sociolinguistics: A Survey. Computational Linguistics, 42(3):537–593. find
  71. Debora Nozza, Federico Bianchi, and Dirk Hovy. 2021. HONEST: Measuring hurtful sentence completion in language models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, Minnesota. Association for Computational Linguisti… find
  72. Jiaxin Pei and David Jurgens. 2020. Quantifying intimacy in language. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5307–5326. find
  73. Vinodkumar Prabhakaran, Owen Rambow, and Mona Diab. 2012. Predicting overt display of power in written dialogs. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 518–522. Association for Computatio… find
  74. Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, and Alan W Black. 2018. Style transfer through back-translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 866–876, Melbourne, Australia. Association for Computat… find our card
  75. Mohammed R. H. Qwaider, Abed Alhakim Freihat, and Fausto Giunchiglia. 2017. TrentoTeam at SemEval-2017 task 3: An application of Grice maxims in ranking community question answers. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 271– 274, Vancouver, Can… find
  76. Bahar Radfar, Karthik Shivaram, and Aron Culotta. 2020. Characterizing variation in toxic language by social context. In Proceedings of the International AAAI Conference on Web and Social Media, volume 14, pages 959–963. find
  77. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8):9. find
  78. Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised user geolocation via graph convolutional networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2009–2019, Melbourne, Australia. Association for Computa… find
  79. Christof Rapp. 2011. Aristotle’s rhetoric. Stanford Encyclopedia of Philosophy. find
  80. Farzana Rashid and Eduardo Blanco. 2017. Dimensions of interpersonal relationships: Corpus and experiments. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2307–2316. find
  81. Farzana Rashid, Tommaso Fornaciari, Dirk Hovy, Eduardo Blanco, and Fernando Vega-Redondo. 2020. Helpful or hierarchical? predicting the communicative strategies of chat participants, and their impact on success. In Findings of the Association for Computational Linguistics: Conference on Empirical Me… find
  82. Hannah Rashkin, Sameer Singh, and Yejin Choi. 2016. Connotation frames: A data-driven investigation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 311–321, Berlin, Germany. Association for Computational Linguistics. find
  83. Alan Ritter, Colin Cherry, and William B. Dolan. 2011. Data-driven response generation in social media. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 583–593, Edinburgh, Scotland, UK. Association for Computational Linguistics. find
  84. Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. 2019a. The risk of racial bias in hate speech detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1668–1678, Florence, Italy. Association for Computational Linguistics. find our card
  85. Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. 2020. Social bias frames: Reasoning about social and power implications of language. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5477–5490, Online. Association… find our card
  86. Christian Stab and Iryna Gurevych. 2014. Identifying argumentative discourse structures in persuasive essays. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 46–56. find our card
  87. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019b. Social iqa: Commonsense reasoning about social interactions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Process… find
  88. Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019. Evaluating gender bias in machine translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1679–1684, Florence, Italy. Association for Computational Linguistics. find our card
  89. Roger C Schank and Robert P Abelson. 1975. Scripts, plans, and knowledge. In IJCAI, volume 75, pages 151–157. find
  90. Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In Thirtieth AAAI Conference on Artificial Intelligence. find our card
  91. Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020. Predictive biases in natural language processing models: A conceptual framework and overview. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5248–5264, Online. Association for Computationa… find
  92. Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2005. Implicit user modeling for personalized search. In Proceedings of the 14th ACM international conference on Information and knowledge management, pages 824–831. find
  93. Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd international conference on world wide web, pages 373– 374. find
  94. Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2019. The woman worked as a babysitter: On biases in language generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (… find our card
  95. Michael Silverstein. 2003. Indexical order and the dialectics of sociolinguistic life. Language & communication, 23(3-4):193–229. find
  96. Matthew Simmons, Lada Adamic, and Eytan Adar. 2011. Memes online: Extracted, subtracted, injected, and recollected. In Proceedings of the International AAAI Conference on Web and Social Media, volume 5. find
  97. Peter Trudgill. 2000. Sociolinguistics: An introduction to language and society. Penguin UK. find
  98. Amos Tversky and Daniel Kahneman. 1973. Availability: A heuristic for judging frequency and probability. Cognitive psychology, 5(2):207–232. find
  99. Eva Vanmassenhove, Dimitar Shterionov, and Andy Way. 2019. Lost in translation: Loss and decay of linguistic richness in machine translation. In Proceedings of Machine Translation Summit XVII Volume 1: Research Track, pages 222–232, Dublin, Ireland. European Association for Machine Translation. find
  100. Svitlana Volkova, Theresa Wilson, and David Yarowsky. 2013. Exploring demographic language variations to improve multilingual sentiment analysis in social media. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1815–1827. find
  101. Byron C. Wallace, Do Kook Choe, Laura Kertz, and Eugene Charniak. 2014. Humans require context to infer ironic intent (so computers probably do, too). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 512–516, Baltimore, Maryla… find
  102. Zijian Wang and David Jurgens. 2018. It’s going to be okay: Measuring access to support in online communities. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 33–45. find
  103. William Warner and Julia Hirschberg. 2012. Detecting hate speech on the world wide web. In Proceedings of the Second Workshop on Language in Social Media, pages 19–26, Montréal, Canada. Association for Computational Linguistics. find
  104. Bolin Wei, Shuai Lu, Lili Mou, Hao Zhou, Pascal Poupart, Ge Li, and Zhi Jin. 2017. Why do neural dialog systems generate short and meaningless replies? A comparison between dialog and translation. CoRR, abs/1712.02250. — [arXiv:1712.02250](https://arxiv.org/abs/1712.02250) find
  105. Ludwig Wittgenstein. 2010. Philosophical investigations. John Wiley & Sons. find
  106. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144. find
  107. Yuwei Wu, Xuezhe Ma, and Diyi Yang. 2021. Personalized Response Generation via Generative Split Memory Network. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies. find
  108. Diyi Yang. 2019. Computational Social Roles. Ph.D. thesis, Ph. D. thesis, Carnegie Mellon University. find
  109. Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, and Eduard Hovy. 2019a. Let’s make your request more persuasive: Modeling persuasive strategies via semi-supervised neural nets on crowdfunding platforms. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Comp… find
  110. Diyi Yang, Robert E Kraut, Tenbroeck Smith, Elijah Mayfield, and Dan Jurafsky. 2019b. Seekers, providers, welcomers, and storytellers: Modeling social roles in online health communities. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–14. find
  111. Diyi Yang, Alon Lavie, Chris Dyer, and Eduard Hovy. 2015. Humor recognition and humor anchor extraction. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2367–2376, Lisbon, Portugal. Association for Computational Linguistics. find
  112. Yi Yang and Jacob Eisenstein. 2017. Overcoming language variation in sentiment analysis with social attention. Transactions of the Association for Computational Linguistics, 5:295–307. find
  113. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 14… find our card
  114. 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), pages 2204– 2213, Melbo… find our card