A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
This work addresses multimodal language understanding for applications like human-computer interaction, but it appears incremental as it builds on existing Transformer architectures with specific modifications.
The paper tackles emotion recognition and sentiment analysis by proposing a Transformer-based joint-encoding method with modular co-attention and a glimpse layer, achieving results evaluated on the CMU-MOSEI dataset as part of the ACL20 challenge.
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.