SubmissionNumber#=%=#98
FinalPaperTitle#=%=#ISDS-NLP at SemEval-2024 Task 10: Transformer based neural networks for emotion recognition in conversations
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Claudiu Creanga
JobTitle#==#
Organization#==#
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.
Author{1}{Firstname}#=%=#Claudiu
Author{1}{Lastname}#=%=#Creanga
Author{1}{Username}#=%=#claudiucreanga
Author{1}{Email}#=%=#claudiu.creanga.backup@gmail.com
Author{1}{Affiliation}#=%=#University of Bucharest
Author{2}{Firstname}#=%=#Liviu P.
Author{2}{Lastname}#=%=#Dinu
Author{2}{Username}#=%=#liviu.p.dinu
Author{2}{Email}#=%=#liviu.p.dinu@gmail.com
Author{2}{Affiliation}#=%=#University of Bucharest

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