Papers by Dushyant Singh Chauhan
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes (2020.aacl-main)
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| Challenge: | Empirical results show the efficacy of our proposed multi-task framework over existing state-of-the-art systems. |
| Approach: | They propose a multi-task, multi-modal deep learning framework to solve multiple tasks simultaneously. |
| Outcome: | The proposed framework performs better than existing state-of-the-art systems on a complicated form of information, i.e., memes. |
Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation (2025.findings-naacl)
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| Challenge: | Existing systems that generate sarcastic explanations neglect the significance of the intended irony in generating them. |
| Approach: | They propose a multimodal sarcasm explanation that leverages the inter-modality relationships between an image and its caption to learn the intended irony for explanations. |
| Outcome: | The proposed model outperforms existing models on the sarcastic post-processing task and shows that it is more accurate than existing models. |
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting (2022.coling-1)
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| Challenge: | Humor is an essential aspect of daily conversation, and people try to provoke humor in their talks. |
| Approach: | They propose a multitask framework that annotates Hindi utterances with sentiment and emotion classes. |
| Outcome: | The proposed framework improves on the recently released Hindi Humor dataset . it takes sentiment and emotion into account to understand humor . |
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis (2020.acl-main)
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| Challenge: | Existing systems for sarcasm detection are limited by the use of sarcasm . sarasm is often used to convey thinly veiled disapproval humorously. |
| Approach: | They propose a multi-task deep learning framework to solve sarcasm problems simultaneously . they manually annotate a sarcsm dataset with sentiment and emotion classes . |
| Outcome: | The proposed framework is able to solve sarcasm, sentiment and emotion problems in a multi-modal conversational scenario. |
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis (D19-1)
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| Challenge: | Multi-modal analysis is a field emerging in the fields of natural language processing, computer vision and speech processing . multimodal analysis uses a variety of information from multiple sources to build efficient systems . acoustic and visual information can provide better information for classification decisions . |
| Approach: | They propose a recurrent neural network based approach for multi-modal sentiment and emotion analysis . they employ a context-aware attention module to exploit the correspondence among neighboring utterances . |
| Outcome: | The proposed model learns inter-modal interaction among participating modalities through auto-encoder mechanism . it is compared with existing state-of-the-art models on five standard multi-modal affect analysis datasets . |