This paper outlines the approach of the ISDS-NLP team in the SemEval 2024
Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF). For
Subtask 1 we obtained a weighted F1 score of 0.43 and placed 12 in the
leaderboard. We investigate two distinct approaches: Masked Language Modeling
(MLM) and Causal Language Modeling (CLM). For MLM, we employ pre-trained
BERT-like models in a multilingual setting, fine-tuning them with a classifier
to predict emotions. Experiments with varying input lengths, classifier
architectures, and fine-tuning strategies demonstrate the effectiveness of this
approach. Additionally, we utilize Mistral 7B Instruct V0.2, a state-of-the-art
model, applying zero-shot and few-shot prompting techniques. Our findings
indicate that while Mistral shows promise, MLMs currently outperform them in
sentence-level emotion classification.

ISDS-NLP 团队在 SemEval 2024 任务 10 中提出了 EDiReF 方法，采用了掩码语言模型和因果语言模型两种不同的方法，研究了多语种设置下对情感进行预测的效果，并发现掩码语言模型在句子级情感分类方面的性能优于 Mistral 模型。