Transformer based neural networks for emotion recognition in conversations

TL;DR

This paper explores transformer-based models for emotion recognition in conversations, comparing MLM and CLM approaches, and evaluates their performance on the SemEval 2024 task, highlighting the strengths of MLMs over large language models like Mistral.

Contribution

It presents a comparative analysis of MLM and CLM methods for emotion recognition, including fine-tuning strategies and zero-shot prompting with Mistral 7B, in a multilingual conversational context.

Findings

MLM approaches achieved a weighted F1 score of 0.43.

MLMs outperform Mistral 7B in sentence-level emotion classification.

Fine-tuning strategies significantly impact model performance.

Abstract

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.