In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7% on the F1 score.
翻译:在本文中,我们描述了一种在代码混合的推文(hindi-english)中预测情绪的方法。我们在科达拉布的团队(Verissimo.manoel)根据四种模型(MultiFiT、BERT、ALBERT和XLNET)的组合制定了一种方法。最后的分类算法是这四种模型中所有软最大值的一些预测组合。这一结构在SemEval 2020挑战(任务9)中被使用和评估,我们的系统在F1评分上获得了72.7%。