%0 Conference Proceedings
%T Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis
%A Akhtar, Md Shad
%A Chauhan, Dushyant
%A Ghosal, Deepanway
%A Poria, Soujanya
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F akhtar-etal-2019-multi
%X Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e. text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI dataset for multi-modal sentiment and emotion analysis. Evaluation results suggest that multi-task learning framework offers improvement over the single-task framework. The proposed approach reports new state-of-the-art performance for both sentiment analysis and emotion analysis.
%R 10.18653/v1/N19-1034
%U https://aclanthology.org/N19-1034
%U https://doi.org/10.18653/v1/N19-1034
%P 370-379