CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

Hazarika, Devamanyu; Poria, Soujanya; Gorantla, Sruthi; Wang, Zhaoxia; Zimmermann, Roger; Mihalcea, Rada

doi:10.48550/arxiv.1805.06413

Cited by 12 publications

(13 citation statements)

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“…incorporated images along with the corresponding captions for detecting inter-modal incongruity. Hazarika et al (2018) extracted contextual information from the discourse of a discussion thread, encoded stylometric and personality features of the users, and subsequently used content-based features for sarcasm detection in online social media. Cai, Cai, and Wan (2019) exploited the multi-stage hierarchical fusion mechanism for multimodal sarcasm detection.…”

Section: Related Workmentioning

confidence: 99%

Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation

Desai¹,

Chakraborty²,

Akhtar³

2022

AAAI

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Sarcasm is a pervading linguistic phenomenon and highly challenging to explain due to its subjectivity, lack of context and deeply-felt opinion. In the multimodal setup, sarcasm is conveyed through the incongruity between the text and visual entities. Although recent approaches deal with sarcasm as a classification problem, it is unclear why an online post is identified as sarcastic. Without proper explanation, end users may not be able to perceive the underlying sense of irony. In this paper, we propose a novel problem -- Multimodal Sarcasm Explanation (MuSE) -- given a multimodal sarcastic post containing an image and a caption, we aim to generate a natural language explanation to reveal the intended sarcasm. To this end, we develop MORE, a new dataset with explanation of 3510 sarcastic multimodal posts. Each explanation is a natural language (English) sentence describing the hidden irony. We benchmark MORE by employing a multimodal Transformer-based architecture. It incorporates a cross-modal attention in the Transformer's encoder which attends to the distinguishing features between the two modalities. Subsequently, a BART-based auto-regressive decoder is used as the generator. Empirical results demonstrate convincing results over various baselines (adopted for MuSE) across five evaluation metrics. We also conduct human evaluation on predictions and obtain Fleiss' Kappa score of 0.4 as a fair agreement among 25 evaluators.

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Section: Related Workmentioning

confidence: 99%

Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation

Desai¹,

Chakraborty²,

Akhtar³

2022

AAAI

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“…Compared to English, only a few studies have been made on Arabic sarcasm detection. Among the studies completed in this area are research by (Riloff et al, 2013;Oprea and Magdy, 2019;Joshi et al, 2016;Bamman and Smith 2015;Campbell and Katz, 2012;Amir et al, 2016;Hazarika et al, 2018). (Oprea and Magdy, 2020) show the effect of sociocultural variables on sarcasm communication online, which makes the performance of models trained on English unpredictable, if they are trained on other languages.…”

Section: Related Workmentioning

confidence: 99%

AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques

Lotfy¹,

Torki²,

El-Makky³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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Sarcasm detection is an important task in Natural Language Understanding. Sarcasm is a form of verbal irony that occurs when there is a discrepancy between the literal and intended meanings of an expression. In this paper, we use the tweets of the Arabic dataset provided by SemEval-2022 task 6 to train deep learning classifiers to solve the sub-tasks A and C associated with the dataset. Sub-task A is to determine if the tweet is sarcastic or not. For sub-task C, given a sarcastic text and its non-sarcastic rephrase, i.e. two texts that convey the same meaning, determine which is the sarcastic one. In our solution, we utilize finetuned MARBERT (Abdul-Mageed et al., 2021) model with an added single linear layer on top for classification. The proposed solution achieved 0.5076 F1-sarcastic in Arabic sub-task A, accuracy of 0.7450 and F-score of 0.7442 in Arabic sub-task C. We achieved the 2 nd and the 9 th places for Arabic sub-tasks A and C respectively.

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“…Therefore, Mukherjee and Bala (2017) report that including features independent of the text leads to ameliorating the performance of sarcasm models. To this end, studies take three forms of context as feature: 1) author context (Hazarika et al, 2018;Bamman and Smith, 2015), 2) conversational context (Wang et al, 2015), and 3) topical context (Ghosh and Veale, 2017). Another popular line of research utilizes various user embedding techniques that encode users' stylometric and personality features to improve their sarcasm detection models (Hazarika et al, 2018).…”

Section: Context-based Modelsmentioning

confidence: 99%

“…To this end, studies take three forms of context as feature: 1) author context (Hazarika et al, 2018;Bamman and Smith, 2015), 2) conversational context (Wang et al, 2015), and 3) topical context (Ghosh and Veale, 2017). Another popular line of research utilizes various user embedding techniques that encode users' stylometric and personality features to improve their sarcasm detection models (Hazarika et al, 2018). Their model, CAS-CADE, utilizes user embeddings that encode stylometric and personality features of the users.…”

Section: Context-based Modelsmentioning

confidence: 99%

Sarcasm Detection: A Comparative Study

Yaghoobian,

Arabnia,

Rasheed

2021

Preprint

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Sarcasm detection is the task of identifying irony 1 containing utterances in sentimentbearing text. However, the figurative and creative nature of sarcasm poses a great challenge for affective computing systems performing sentiment analysis. This article compiles and reviews the salient work in the literature of automatic sarcasm detection. Thus far, three main paradigm shifts have occurred in the way researchers have approached this task: 1) semisupervised pattern extraction to identify implicit sentiment, 2) use of hashtag-based supervision, and 3) incorporation of context beyond target text. In this article, we provide a comprehensive review of the datasets, approaches, trends, and issues in sarcasm and irony detection.

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CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

Cited by 12 publications

References 0 publications

Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation

Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation

AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques

Sarcasm Detection: A Comparative Study

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