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A Transformer-based Joint-encoding For Emotion Recognition And Sentiment Analysis

Jean-Benoit Delbrouck, Noé Tits, Mathilde Brousmiche, Stéphane Dupont . Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML) 2020 – 83 citations

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Affective Computing Datasets Has Code Image Text Integration Model Architecture Visual Contextualization

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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