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Found 6 papers, 1 papers with code
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting
We design a multitasking framework wherein we first propose a Context Transformer to capture the deep contextual relationships with the input utterances.
M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations.
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes
The main motivation of iTRM is to learn the relationship between the tasks to realize how they help each other.
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis
In this paper, we hypothesize that sarcasm is closely related to sentiment and emotion, and thereby propose a multi-task deep learning framework to solve all these three problems simultaneously in a multi-modal conversational scenario.
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis
In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis.
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis
In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both.
