“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 1998–2010 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 1998 “Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space Debanjan Ghosh∗1 Ritvik Shrivastava∗2, 3 Smaranda Muresan3 1Educational Testing Service 2MindMeld, Cisco Systems 3Data Science Institute, Columbia University dghosh@ets.org, ritvshri@cisco.com, {rs3868, smara}@columbia.edu Abstract Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figura- tive language, such as sarcasm, either as per- suasive devices or to attack the opponent by an ad hominem argument. To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a cor- pus annotated with both argumentative moves (agree/disagree) and sarcasm. We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classi- fication (agree/disagree/none), and (b) multi- task learning for argumentative relation clas- sification and sarcasm detection using deep learning architectures (e.g., dual Long Short- Term Memory (LSTM) with hierarchical atten- tion and Transformer-based architectures). We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups. 1 Introduction User-generated conversational data such as dis- cussion forums provide a wealth of naturally oc- curring arguments. The ability to automatically detect and classify argumentative relations (e.g., agree/disagree) in threaded discussions is useful to understand how collective opinions form, how conflict arises and is resolved (van Eemeren et al., 1993; Abbott et al., 2011; Walker et al., 2012b; Misra and Walker, 2013; Ghosh et al., 2014; Rosen- thal and McKeown, 2015; Stede and Schneider, 2018). Linguistic and argumentation theories have thoroughly studied the use of sarcasm in argumen- tation, including its effectiveness as a persuasive device or as a means to express an ad hominem ∗Equal Contribution. Arg. Rel. Turn Pairs Prior Turn: Today, no informed creationist would deny natural selection. Agree Current Turn: Seeing how this was pro- posed over a century and a half ago by Dar- win, what took the creationists so long to catch up? Prior Turn: Personally I wouldn’t own a gun for self defense because I am just not that big of a sissy. Disagree Current Turn: Because taking responsibil- ity for ones own safety is certainly a sissy thing to do? Prior Turn: I’m not surprised that no one on your side of the debate would correct you, but wolves and dogs are both members of the same species. The Canid species. Current Turn: Wow, you ’re even wrong when you get away from your precious Bible and try to sound scientific. Prior Turn: The hand of God kept me from serious harm. Maybe He has a plan for me. None Current Turn: You better hurry up . Are n’t you like 113 years old. Table 1: Sarcastic turns that disagree, agree or have no argumentative relation with their prior turns. fallacy (attacking …

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