A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
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.
PDF Abstract WS 2020 PDF WS 2020 AbstractCode
Colab
Datasets
CMU-MOSEI
Results from the Paper
Ranked #5 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(using extra training data)
