A Report on the 2020 Sarcasm Detection Shared Task

Ghosh, Debanjan; Vajpayee, Avijit; Muresan, Smaranda

doi:10.18653/v1/2020.figlang-1.1

Cited by 27 publications

(38 citation statements)

References 53 publications

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“…The LSTM models are influenced by Ghosh and Veale (2017); , where the function of contextual knowledge is used to detect sarcasm. Lastly, transformer models such as BERT and RoBERTa have been used in the winning entries for the recent shared task on sarcasm detection (Ghosh et al, 2020). In our research, for both kinds of deep-learning models, the best results are obtained by using the multitask setup, showing that multitask learning indeed helps improve both tasks.…”

Section: Related Workmentioning

confidence: 72%

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

Ghosh¹,

Shrivastava²,

Muresan³

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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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.

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Section: Related Workmentioning

confidence: 72%

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

Ghosh¹,

Shrivastava²,

Muresan³

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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“…Given that SDC is a new area, the benchmark datasets are relatively limited. In this work, we perform experiments on the MUStARD 2 [2] and the 2020 sarcasm detection Reddit track 3 [68] datasets. MUStARD comprises 690 videos from several sources, e.g., Big Bang Theory, Friends, etc.…”

Section: A Experimental Settingsmentioning

confidence: 99%

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

Zhang

Liu

et al. 2021

IEEE Trans. Fuzzy Syst.

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Sarcasm detection in conversation (SDC), a theoretically and practically challenging artificial intelligence (AI) task, aims to discover elusively ironic, contemptuous and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected the intrinsic vagueness and uncertainty of human language in emotional expression and understanding. To address this gap, we propose a complex-valued fuzzy network (CFN) by leveraging the mathematical formalisms of quantum theory (QT) and fuzzy logic. In particular, the target utterance to be recognized is considered as a quantum superposition of a set of separate words. The contextual interaction between adjacent utterances is described as the interaction between a quantum system and its surrounding environment, constructing the quantum composite system, where the weight of interaction is determined by a fuzzy membership function. In order to model both the vagueness and uncertainty, the aforementioned superposition and composite systems are mathematically encapsulated in a density matrix. Finally, a quantum fuzzy measurement is performed on the density matrix of each utterance to yield the probabilistic outcomes of sarcasm recognition. Extensive experiments are conducted on the MUStARD and the 2020 sarcasm detection Reddit track datasets, and the results show that our model outperforms a wide range of strong baselines.

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“…While these models have been applied successfully to a wide range of Natural Language Processing (NLP) tasks, such as sentiment analysis (Sun et al, 2019) and word similarity computation (Zhang et al, 2019b), they have not been fully exploited for the more challenging problem of sarcasm detection. A few studies that proposed to use these models (Mozafari et al, 2019;Castro et al, 2019), utilize the already pre-trained embeddings, which are not optimized for sarcasm detection, and their performance can be further improved (Potamias et al, 2019;Ghosh et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Affective and Contextual Embedding for Sarcasm Detection

Babanejad¹,

Davoudi²,

An³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a challenging task, due to a lack of any vocal intonation or facial gestures in textual data. To date, most of the approaches to addressing the problem have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec. However, these models inherit limitations that render them inadequate for the task of sarcasm detection. In this paper, we propose two novel deep neural network models for sarcasm detection, namely ACE 1 and ACE 2. Given as input a text passage, the models predict whether it is sarcastic (or not). Our models extend the architecture of BERT by incorporating both affective and contextual features. To the best of our knowledge, this is the first attempt to directly alter BERT's architecture and train it from scratch to build a sarcasm classifier. Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.

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A Report on the 2020 Sarcasm Detection Shared Task

Cited by 27 publications

References 53 publications

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

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

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

Affective and Contextual Embedding for Sarcasm Detection

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