@inproceedings{chauhan-etal-2019-context,
title = "Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis",
author = "Chauhan, Dushyant Singh and
Akhtar, Md Shad and
Ekbal, Asif and
Bhattacharyya, Pushpak",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1566",
doi = "10.18653/v1/D19-1566",
pages = "5647--5657",
abstract = "In recent times, multi-modal analysis has been an emerging and highly sought-after field at the intersection of natural language processing, computer vision, and speech processing. The prime objective of such studies is to leverage the diversified information, (e.g., textual, acoustic and visual), for learning a model. The effective interaction among these modalities often leads to a better system in terms of performance. In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis. The proposed model learns the inter-modal interaction among the participating modalities through an auto-encoder mechanism. We employ a context-aware attention module to exploit the correspondence among the neighboring utterances. We evaluate our proposed approach for five standard multi-modal affect analysis datasets. Experimental results suggest the efficacy of the proposed model for both sentiment and emotion analysis over various existing state-of-the-art systems.",
}
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%0 Conference Proceedings
%T Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis
%A Chauhan, Dushyant Singh
%A Akhtar, Md Shad
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chauhan-etal-2019-context
%X In recent times, multi-modal analysis has been an emerging and highly sought-after field at the intersection of natural language processing, computer vision, and speech processing. The prime objective of such studies is to leverage the diversified information, (e.g., textual, acoustic and visual), for learning a model. The effective interaction among these modalities often leads to a better system in terms of performance. In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis. The proposed model learns the inter-modal interaction among the participating modalities through an auto-encoder mechanism. We employ a context-aware attention module to exploit the correspondence among the neighboring utterances. We evaluate our proposed approach for five standard multi-modal affect analysis datasets. Experimental results suggest the efficacy of the proposed model for both sentiment and emotion analysis over various existing state-of-the-art systems.
%R 10.18653/v1/D19-1566
%U https://aclanthology.org/D19-1566
%U https://doi.org/10.18653/v1/D19-1566
%P 5647-5657
Markdown (Informal)
[Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis](https://aclanthology.org/D19-1566) (Chauhan et al., EMNLP-IJCNLP 2019)
ACL
- Dushyant Singh Chauhan, Md Shad Akhtar, Asif Ekbal, and Pushpak Bhattacharyya. 2019. Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5647–5657, Hong Kong, China. Association for Computational Linguistics.