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。

基于 Transformer 的对话情绪识别的神经网络

Transformer based neural networks for emotion recognition in  conversations

This paper presents our approach for the SemEval-2024 Task 10: Emotion
Discovery and Reasoning its Flip in Conversations. For the Emotion Recognition
in Conversations (ERC) task, we utilize a masked-memory network along with
speaker participation. We propose a transformer-based speaker-centric model for
the Emotion Flip Reasoning (EFR) task. We also introduce Probable Trigger Zone,
a region of the conversation that is more likely to contain the utterances
causing the emotion to flip. For sub-task 3, the proposed approach achieves a
5.9 (F1 score) improvement over the task baseline. The ablation study results
highlight the significance of various design choices in the proposed method.

本研究提出了一种基于遮蔽记忆网络和参与讲话者的转换器模型，用于在对话中的情感识别及情感变化推理，并介绍了可能触发情感变化的对话区域。该方法在第三子任务中相对任务基准模型提高了 5.9（F1 分数），故设计选择在该方法中具备重要意义。