Papers by Dushyant Chauhan

2 papers
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis (D18-1)

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Challenge: Existing methods for multi-modal sentiment analysis are limited due to the use of text, visual and acoustic inputs.
Approach: They propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level sentiment prediction.
Outcome: The proposed framework performs better on two multi-modal sentiment analysis benchmark datasets with accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets.
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis (N19-1)

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Challenge: Existing frameworks for sentiment and emotion analysis are not efficient for inter-task learning.
Approach: They propose a multi-task learning framework that performs sentiment and emotion analysis together.
Outcome: The proposed framework improves on a CMU-MOSEI dataset for sentiment and emotion analysis.

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