Transformer based neural networks for emotion recognition in conversations
<|reference_start|>Transformer based neural networks for emotion recognition in conversations: 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.<|reference_end|>
@article{creanga2024transformer,
    title={Transformer based neural networks for emotion recognition in
  conversations},
    author={Claudiu Creanga, Liviu P. Dinu},
    journal={arXiv preprint arXiv:2405.11222},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11222},
    primaryClass={cs.CL}
}