A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis

• URL: http://arxiv.org/abs/2006.15955v1
• Date: Mon, 29 Jun 2020 11:51:46 GMT
• Title: A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
• Authors: Jean-Benoit Delbrouck and No\'e Tits and Mathilde Brousmiche and St\'ephane Dupont
• Abstract summary: This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities.
• Score: 8.927538538637783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source: https://github.com/jbdel/MOSEI_UMONS.

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