A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis

Jean-Benoit Delbrouck, Noé Tits, Mathilde Brousmiche, Stéphane Dupont


Abstract
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 .
Anthology ID:
2020.challengehml-1.1
Volume:
Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)
Month:
July
Year:
2020
Address:
Seattle, USA
Venues:
ACL | Challenge-HML | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://www.aclweb.org/anthology/2020.challengehml-1.1
DOI:
10.18653/v1/2020.challengehml-1.1
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.challengehml-1.1.pdf
Video:
 http://slideslive.com/38931263