Persuasive Natural Language Generation - A Literature Review

This literature review focuses on the use of Natural Language Generation (NLG) to automatically detect and generate persuasive texts. Extending previous research on automatic identification of persuasion in text, we concentrate on generative aspects through conceptualizing determinants of persuasion in five business-focused categories: benevolence, linguistic appropriacy, logical argumentation, trustworthiness, tools & datasets. These allow NLG to increase an existing message’s persuasiveness. Previous research illustrates key aspects in each of the above mentioned five categories. A research agenda to further study persuasive NLG is developed. The review includes analysis of seventy-seven articles, outlining the existing body of knowledge and showing the steady progress in this research field.

[1]  Richard T. Watson,et al.  Analyzing the Past to Prepare for the Future: Writing a Literature Review , 2002, MIS Q..

[2]  Jon M. Kleinberg,et al.  Echoes of power: language effects and power differences in social interaction , 2011, WWW.

[3]  Michel Fayol,et al.  Processing interclausal relationships : studies in the production and comprehension of text , 1997 .

[4]  A. Duhachek,et al.  Artificial Intelligence and Persuasion: A Construal-Level Account , 2020, Psychological science.

[5]  Mingda Zhang,et al.  Interpreting the Rhetoric of Visual Advertisements , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jiyou Jia,et al.  CSIEC: A computer assisted English learning chatbot based on textual knowledge and reasoning , 2009, Knowl. Based Syst..

[7]  M. Burgoon,et al.  Language expectancy theory. , 2002 .

[8]  Preslav Nakov,et al.  Experiments in Detecting Persuasion Techniques in the News , 2019, ArXiv.

[9]  Asif Ekbal,et al.  A deep neural network based multi-task learning approach to hate speech detection , 2020, Knowl. Based Syst..

[10]  D. McNamara,et al.  Assessing Text Readability Using Cognitively Based Indices , 2008 .

[11]  Siham El Kihal,et al.  Using Natural Language Processing to Investigate the Role of Syntactic Structure in Persuasive Marketing Communication , 2019 .

[12]  A. Marty.  Getting to YES. Negotiating Agreement Without Giving In , 1983 .

[13]  Claes H. de Vreese,et al.  Using a Personality-Profiling Algorithm to Investigate Political Microtargeting: Assessing the Persuasion Effects of Personality-Tailored Ads on Social Media , 2020, Communication Research.

[14]  C. Voss,et al.  Never Split the Difference: Negotiating as if Your Life Depended on It , 2016 .

[15]  Claire Cardie,et al.  Exploring the Role of Argument Structure in Online Debate Persuasion , 2020, EMNLP.

[16]  W. Brewer.  Literary Theory, Rhetoric, and Stylistics: Implications for Psychology , 2017 .

[17]  Bernadette Longo,et al.  The Role of Metadiscourse in Persuasion. , 1994 .

[18]  Derek D. Rucker,et al.  Persuasion, Emotion, and Language: The Intent to Persuade Transforms Language via Emotionality , 2018, Psychological science.

[19]  P. Catellani,et al.  How expert witnesses' counterfactuals influence causal and responsibility attributions of mock jurors and expert judges , 2020 .

[20]  Gözde Özbal,et al.  Echoes of Persuasion: The Effect of Euphony in Persuasive Communication , 2015, NAACL.

[21]  Danielle S McNamara,et al.  Natural language processing in an intelligent writing strategy tutoring system , 2012, Behavior Research Methods.

[22]  Zhou Yu,et al.  Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good , 2019, ACL.

[23]  K. Cameron,et al.  A practitioner's guide to persuasion: an overview of 15 selected persuasion theories, models and frameworks. , 2009, Patient education and counseling.

[24]  Juan M. Corchado,et al.  Constructing deliberative agents with case‐based reasoning technology , 2003, Int. J. Intell. Syst..

[25]  U. Hasson,et al.  Speaker–listener neural coupling underlies successful communication , 2010, Proceedings of the National Academy of Sciences.

[26]  Arjun Mukherjee,et al.  Pro/Con: Neural Detection of Stance in Argumentative Opinions , 2019, SBP-BRiMS.

[27]  E. Higgins.  Making a good decision: value from fit. , 2000, The American psychologist.

[28]  R. Petty,et al.  Challenging Moral Attitudes With Moral Messages , 2019, Psychological science.

[29]  M. A. Britt,et al.  Constructing representations of arguments , 2003 .

[30]  G. Marwell,et al.  Dimensions of Compliance-Gaining Behavior: An Empirical Analysis , 1967 .

[31]  Allen Roush,et al.  DebateSum: A large-scale argument mining and summarization dataset , 2020, ARGMINING.

[32]  Simon Kerl.  A comprehensive grammar of the English language , .

[33]  Ralph Bergmann,et al.  Clustering of Argument Graphs Using Semantic Similarity Measures , 2019, KI.

[34]  D. Helbing,et al.  The rippling dynamics of valenced messages in naturalistic youth chat , 2018, Behavior Research Methods.

[35]  Iryna Gurevych,et al.  Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! , 2018, COLING.

[36]  Michael J. Prietula,et al.  Getting to best: efficiency versus optimality in negotiation , 2000, Cogn. Sci..

[37]  Ya'akov Gal,et al.  The Effects of Goal Revelation on Computer-Mediated Negotiation , 2009 .

[38]  Marie-Francine Moens,et al.  Argumentation mining: How can a machine acquire common sense and world knowledge? , 2018, Argument Comput..

[39]  Cristian Danescu-Niculescu-Mizil,et al.  Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions , 2016, WWW.

[40]  Morten H. Christiansen,et al.  of Experimental Psychology : Learning , Memory , and Cognition , 2019 .

[41]  Gregory J. Park,et al.  Automatic personality assessment through social media language. , 2015, Journal of personality and social psychology.

[42]  Iryna Gurevych,et al.  Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM , 2016, ACL.

[43]  Freimut Bodendorf,et al.  Warning system for online market research - Identifying critical situations in online opinion formation , 2011, Knowl. Based Syst..

[44]  Yiya Chen,et al.  Let’s you do that: Sharing the cognitive burdens of dialogue , 2007 .

[45]  Avelino J. Gonzalez,et al.  Context‐Centric Speech‐Based Human–Computer Interaction , 2013, Int. J. Intell. Syst..

[46]  E. Harmon-Jones,et al.  A Cognitive Dissonance Theory Perspective on Persuasion , 2002 .

[47]  R. Cialdini.  Influence: Science and Practice , 1984 .

[48]  Carlo Strapparava,et al.  Resources for Persuasion , 2008, LREC.

[49]  Matthias Hagen,et al.  Data Acquisition for Argument Search: The args.me Corpus , 2019, KI.

[50]  Walter Kintsch,et al.  Toward a model of text comprehension and production. , 1978 .

[51]  I. Douven,et al.  Best, Second-Best, and Good-Enough Explanations: How They Matter to Reasoning , 2018, Journal of experimental psychology. Learning, memory, and cognition.

[52]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[53]  Christiane Fellbaum,et al.  A Semantic Network of English: The Mother of All WordNets , 1998, Comput. Humanit..

[54]  Matthew D. Lieberman,et al.  Putting Feelings Into Words , 2007, Psychological science.

[55]  D. O’Keefe.  Persuasion : theory & research , 2002 .

[56]  Livio Robaldo,et al.  The Penn Discourse Treebank 2.0 Annotation Manual , 2007 .

[57]  Douglas W. Oard,et al.  Believe Me - We Can Do This! Annotating Persuasive Acts in Blog Text , 2011, Computational Models of Natural Argument.

[58]  B. Bahrami,et al.  Social influence protects collective decision making from equality bias. , 2016, Journal of experimental psychology. Human perception and performance.

[59]  Lina Zhou,et al.  Representation and Reasoning Under Uncertainty in Deception Detection: A Neuro-Fuzzy Approach , 2008, IEEE Transactions on Fuzzy Systems.

[60]  Deepak Malhotra,et al.  Evidence for the Pinocchio Effect: Linguistic Differences Between Lies, Deception by Omissions, and Truths , 2012 .

[61]  Sven Laumer,et al.  A Literature Review on Enterprise Social Media Collaboration in Virtual Teams: Challenges, Determinants, Implications and Impacts , 2016, CPR.

[62]  Katia Sycara,et al.  An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text , 2019, ArXiv.

[63]  Carlo Strapparava,et al.  Valentino: A Tool for Valence Shifting of Natural Language Texts , 2008, LREC.

[64]  Ricardo A. Minervino,et al.  Attending to individual recipients’ knowledge when generating persuasive analogies , 2017 .

[65]  Floriana Grasso,et al.  Recent advances in computational models of natural argument , 2007, Int. J. Intell. Syst..

[66]  Björn Niehaves,et al.  Reconstructing the giant: On the importance of rigour in documenting the literature search process , 2009, ECIS.

[67]  Michael Wilson.  MRC Psycholinguistic Database , 2001 .

[68]  Ying Shen,et al.  Multitask Learning and Reinforcement Learning for Personalized Dialog Generation: An Empirical Study , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[69]  G. Miller,et al.  An Expectancy Interpretation of Language and Persuasion , 2018, Recent Advances in Language, Communication, and Social Psychology.

[70]  Henry T. Gilbert.  Persuasion detection in conversation , 2010 .

[71]  Anthony Hunter,et al.  Towards Computational Persuasion via Natural Language Argumentation Dialogues , 2019, KI.

[72]  Mohammad Ali Badamchizadeh,et al.  Ant-Inspired Fuzzily Deceptive Robots , 2016, IEEE Transactions on Fuzzy Systems.

[73]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[74]  Matthias Hagen,et al.  TARGER: Neural Argument Mining at Your Fingertips , 2019, ACL.

[75]  L. Tiedens,et al.  Get mad and get more than even : When and why anger expression is effective in negotiations , 2006 .

[76]  Robert S. Wyer,et al.  Quantitative Prediction of Belief and Opinion Change: A Further Test of a Subjective Probability Model. , 1970 .

[77]  Helmut Horacek,et al.  Argumentation within deductive reasoning , 2007, Int. J. Intell. Syst..