We propose VADEC, a multi-task framework that exploits the correlation
between the categorical and dimensional models of emotion representation for
better subjectivity analysis. Focusing primarily on the effective detection of
emotions from tweets, we jointly train multi-label emotion classification and
multi-dimensional emotion regression, thereby utilizing the inter-relatedness
between the tasks. Co-training especially helps in improving the performance of
the classification task as we outperform the strongest baselines with 3.4%,
11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores
respectively on the AIT dataset. We also achieve state-of-the-art results with
11.3% gains averaged over six different metrics on the SenWave dataset. For the
regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5%
gains in Pearson Correlation scores over the current state-of-the-art on the
EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions
respectively. We conclude our work with a case study on COVID-19 tweets posted
by Indians that further helps in establishing the efficacy of our proposed
solution.

提出了一种多任务框架 VADEC，用于更好地进行情感分析，其中包括多标签情感分类和多维情感回归，并通过协同训练利用任务之间的相互关系，其性能超过了最强基线，尤其是在情感分类任务方面，为 AIT 数据集中的 Jaccard Accuracy、Macro-F1 和 Micro-F1 得分分别带来了 3.4%、11% 和 3.9% 的提高，在 SenWave 数据集上表现出了 11.3% 的平均增益。当在 SenWave 数据集上训练时，VADEC 对于 Valence（V）和 Dominance（D）情感维度在 EMOBANK 数据集上实现了 7.6% 和 16.5% 的皮尔逊相关系数增益，最后在印度发布的 COVID-19 tweet 的案例研究中证明了该框架的有效性。