Deep Models for Converting Sarcastic Utterances into their Non Sarcastic Interpretation

Dubey, Abhijeet; Joshi, Aditya; Bhattacharyya, Pushpak

doi:10.1145/3297001.3297043

Cited by 11 publications

(10 citation statements)

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“…Beyond Sarcasm Identification: While studies in computational sarcasm have predominantly focused on sarcasm identification, some forays have been made into other domains of figurative language analysis. Dubey et al (2019) initiated the work of converting sarcastic utterances into their non-sarcastic interpretations using deep learning.…”

Section: Sarcasm and Multimodalitymentioning

confidence: 99%

“…Consequently, many studies in the domain of dialogue systems have investigated sarcasm from textual, multimodal, and conversational standpoints (Ghosh et al, 2018;Castro et al, 2019;Oraby et al, 2017;Bedi et al, 2021). However, baring some exceptions (Mishra et al, 2019;Dubey et al, 2019;Chakrabarty et al, 2020), research on figurative language has focused predominantly on its identification rather than its comprehension and normalization. This paper addresses this gap by attempting to generate natural language explanations of satirical dialogues.…”

Section: Introductionmentioning

confidence: 99%

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When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues

Kumar¹,

Kulkarni²,

Akhtar³

et al. 2022

Preprint

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Indirect speech such as sarcasm achieves a constellation of discourse goals in human communication. While the indirectness of figurative language warrants speakers to achieve certain pragmatic goals, it is challenging for AI agents to comprehend such idiosyncrasies of human communication. Though sarcasm identification has been a well-explored topic in dialogue analysis, for conversational systems to truly grasp a conversation's innate meaning and generate appropriate responses, simply detecting sarcasm is not enough; it is vital to explain its underlying sarcastic connotation to capture its true essence. In this work, we study the discourse structure of sarcastic conversations and propose a novel task -Sarcasm Explanation in Dialogue (SED). Set in a multimodal and code-mixed setting, the task aims to generate natural language explanations of satirical conversations. To this end, we curate WITS, a new dataset to support our task. We propose MAF (Modality Aware Fusion), a multimodal context-aware attention and global information fusion module to capture multimodality and use it to benchmark WITS. The proposed attention module surpasses the traditional multimodal fusion baselines and reports the best performance on almost all metrics. Lastly, we carry out detailed analyses both quantitatively and qualitatively.

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Section: Sarcasm and Multimodalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues

Kumar¹,

Kulkarni²,

Akhtar³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Authors highlighted that consideration of larger corpus increases context and perform better in terms of accuracy. Dubey et al [20] converted the sarcastic expressions into their literal expressions. Apart from the rule-, and statistical machine learning translationbased approaches, they considered deep learningbased techniques, such as encoder decoder-, attention-, and pointer generator-networks.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Kamal

Abulaish

2021

Cogn Comput

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Sarcasm detection has been a well-studied problem for the computational linguistic researchers. However, research related to different categories of sarcasm has still not gained much attention. Self-Deprecating Sarcasm (SDS) is a special category of sarcasm in which users apply sarcasm over themselves, and it is extensively used in social media platforms, mainly as an advertising tool for the brand endorsement, product campaign, and digital marketing with an aim to increase the sales volume. In this paper, we present a deep learning approach for detecting SDS on Twitter. We propose a novel Convolution and Attention with Bi-directional Gated Recurrent Unit (CAT-BiGRU) model, which consists of an input, embedding, convolutional, Bi-directional Gated Recurrent Unit (BiGRU), and two attention layers. The convolutional layer extracts SDS-based syntactic and semantic features from the embedding layer, BiGRU layer retrieves contextual information from the extracted features in both preceding and succeeding directions, and attention layers are used to retrieve SDS-based comprehensive context representation from the input texts. Finally, sigmoid function is employed to classify the input texts as a self-deprecating or non-self-deprecating sarcasm. Experiments are conducted over seven Twitter datasets to evaluate the proposed (CAT-BiGRU) model using standard evaluation metrics. The experimental results are impressive and significantly better than many neural network-based baselines and state-of-the-art methods. In this paper, we have highlighted biologically inspired and psychologically motivated basis of the proposed approach to examine its affective capabilities with respect to SenticNet. The efficacy of the proposed model is evaluated on two SenticNet-based sentic computing resources-Amazon word embedding and AffectiveSpace. Based on the experimental results, we conclude that deep learning-based approaches have potential to detect SDS in social media texts accurately.

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“…We draw the difference between MuSE and the non-sarcastic interpretation task proposed by (Dubey, Joshi, and Bhattacharyya 2019) in Figure 1. The first difference is the incorporation of multimodality in MuSE compared to the text-based non-sarcastic interpretation.…”

Section: Novelty Of Musementioning

confidence: 99%

Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation

Desai¹,

Chakraborty²,

Akhtar³

2021

Preprint

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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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Deep Models for Converting Sarcastic Utterances into their Non Sarcastic Interpretation

Cited by 11 publications

References 10 publications

When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues

When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation

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