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

2020-06-29 11:51:46
Jean-Benoit Delbrouck, Noé Tits, Mathilde Brousmiche, Stéphane Dupont
arXiv_CL
             
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: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2006.15955

PDF

https://arxiv.org/pdf/2006.15955.pdf

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