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)
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 .