论文标题
基于变压器的联合编码，用于情绪识别和情感分析
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
论文作者
论文摘要
理解表达的情感和情感是人类多模式语言的两个关键因素。本文介绍了基于变压器的联合编码（TBJE），用于情绪识别和情感分析的任务。除了使用变压器体系结构外，我们的方法还依赖于模块化的共同注意力和瞥见层来共同编码一种或多种模式。提出的解决方案也已提交给ACL20：第二个关于多模式语言的大挑战，将在CMU-Mosei数据集上进行评估。复制提出的实验的代码是开源：https：//github.com/jbdel/mosei_umons。
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.
