  This paper aims to bring a new lightweight yet powerful solution for the task
of Emotion Recognition and Sentiment Analysis. Our motivation is to propose two
architectures based on Transformers and modulation that combine the linguistic
and acoustic inputs from a wide range of datasets to challenge, and sometimes
surpass, the state-of-the-art in the field. To demonstrate the efficiency of
our models, we carefully evaluate their performances on the IEMOCAP, MOSI,
MOSEI and MELD dataset. The experiments can be directly replicated and the code
is fully open for future researches.
