Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition
Computation and Language
2020-10-06 v1 Human-Computer Interaction
Machine Learning
Abstract
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.
Cite
@article{arxiv.2010.02057,
title = {Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition},
author = {Jean-Benoit Delbrouck and Noé Tits and Stéphane Dupont},
journal= {arXiv preprint arXiv:2010.02057},
year = {2020}
}
Comments
EMNLP 2020 workshop: NLP Beyond Text (NLPBT)