BITS Pilani at S em E val-2024 Task 10: Fine-tuning BERT and Llama 2 for Emotion Recognition in Conversation (2024)

Abstract: Emotion Recognition in Conversation (ERC)aims to assign an emotion to a dialogue in aconversation between people. The first subtaskof EDiReF shared task aims to assign an emo-tions to a Hindi-English code mixed conversa-tion. For this, our team proposes a system toidentify the emotion based on fine-tuning largelanguage models on the MaSaC dataset. Forour study we have fine tuned 2 LLMs BERTand Llama 2 to perform sequence classificationto identify the emotion of the text.

## introduction emotion can be defined as a conscious mental reaction subjectively experienced as strong feeling usually directed toward a specific object and typically accompanied by physiological and behavioral changes in the body @xcite . in recent times emotion recognition and sentiment analysis has become increasingly popular due to the research developments in natural language processing. although similar to sentiment analysis, while sentiment analysis aims to classify text as positive, negative and neutral, erc aims to identify text as more in-depth emotions like joy, sadness, anger, contempt etc. emotion recognition has multiple use cases in the real world. opinion mining of conversational data posted by users is done at a large scale at big tech companies. @xcite mentions that erc has major potential to be used in healthcare systems for psychological analysis and education to understand student frustrations. it is important for language models and chat bots to understand the sentiment of an input text to respond accordingly and generate empathetic dialogue systems @xcite . for the first subtask of the semeval 2024 task 10: emotion discovery and reasoning its flip in conversation (ediref) @xcite on codalab @xcite , we aim to conduct emotion recognition in conversation on a hindi-english code-mixed dataset. our team proposes a system for this where we fine tune two large language models. namely the transformer based bert @xcite and llama 2 @xcite . all of our code can be found on github at github.com/dipsivenkatesh/ semeval-2024-task-10 2 background ## results for evaluation, the organizers rank the system based on weighted f1 score. this is due to the classes being highly imbalanced in the data distribution. the bert model which was submitted to the leader board achieved a 0.42 weighted f1 score to get 14 th place 4 . the performance of all the models can be found in