  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.
