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

Abstract

Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive 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 corpus 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 classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures). We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups. * Equal Contribution. Arg. Rel. Turn Pairs Prior Turn: Today, no informed creationist would deny natural selection. Agree Current Turn: Seeing how this was proposed over a century and a half ago by Darwin, 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 responsibility 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. N one Current Turn: You better hurry up . Are n't you like 113 years old.

Do Differences in Values Influence Disagreements in Online Discussions?

“…Existing work on agreement analysis is aimed at (1) identifying language that indicates disagreement (e.g., Niculae and Danescu-Niculescu-Mizil, 2016;Wojatzki et al, 2018;Fischer et al, 2022), (2) leveraging stylistic choices like sarcasm for detecting disagreement (Ghosh et al, 2021), (3) finding stance and target pairs, followed by the traditional stance classification (e.g., Chen et al, 2019;De Kock and Vlachos, 2021), and (4) mixing de-tailed opinion information using e.g., logic of evaluation (Draws et al, 2022). Recently, adding social role context to textual comments was shown to have a positive impact on the agreement analysis task (Luo et al, 2023), which indicates the usefulness of background information.…”

Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing

“…The pre-trained Bidirectional Encoder Representations from Transformer (BERT) (Devlin et al, 2019) model has shown to be effective in various NLP models including sentence classification and sentence-pair classification. BERT has also produced excellent results in various argument mining tasks (Chakrabarty et al, 2019;Reimers et al, 2019;Ghosh et al, 2021). In this work, we leverage the standard pre-trained BERT model (bert-baseduncased) (Devlin et al, 2019) to create the model for our revision classification task.…”

RDVI: A Retrieval–Detection Framework for Verbal Irony Detection

“…Accurate and automated identification of verbal irony may allow users' genuine intentions to be understood, thereby facilitating numerous tasks in natural language processing, including e.g. sentiment analysis [6], hate speech detection [7], and argument detection [8].…”