YNU - HPCC at S em E val-2024 Task10: Pre-trained Language Model for Emotion Discovery and Reasoning its Flip in Conversation (2024)

Abstract: This paper describes the application of fine-tuning pre-trained models for SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF), which requires the prediction of emotions for each utterance in a conversation and the identification of sentences where an emotional flip occurs. This model is built on the DeBERTa transformer model and enhanced for emotion detection and flip reasoning in conversations. It employs specific separators for utterance processing and utilizes specific padding to handle variable-length inputs. Methods such as R-drop, back translation, and focalloss are also employed in the training of my model. The model achieved specific results on the competition’s official leaderboard. The code of this paper is available athttps://github.com/jiaowoobjiuhao/SemEval-2024-task10.

## introduction navigating the complexities of emotional dynamics within conversations presents a formidable challenge in natural language processing (nlp). human interactions are characterized by rapid emotional shifts, influenced by context and subtle linguistic nuances, requiring sophisticated models for accurate capture and interpretation. thus, understanding and precisely identifying emotions, especially within conversations marked by emotional transitions, is a significant and challenging endeavor in nlp research. the semeval-2024 competition introduces the emotion discovery and reasoning its flip in conversations (ediref) task @xcite , divided into three subtasks designed to explore the nuanced landscape of emotional dynamics within dialogues: as shown in the first three columns in in the previous sentiment analysis work, various hand-crafted features and sentiment lexicons were utilized to construct solution systems. these systems were developed by integrating traditional methods such as naive bayes, support vector machines (svm) @xcite , and decision trees @xcite . following the advent of deep learning, convolutional neural networks (cnns) @xcite , based on long short-term memory (lstm) @xcite )architectures, were employed for sentence classification tasks. additionally, glove @xcite was utilized for learning sentence features, and bidirectional long short-term memory (bi-lstm) @xcite models were applied to sentence classification to enhance performance. however, these methods encountered challenges in effectively capturing the contextual information of longer texts. with the progression toward larger models, bert-based large-scale pre-training models marked a significant breakthrough in sentiment analysis @xcite this study proposes a deep learning system for task 10 in semeval-2024. we use a • we introduce a foundational model utilizing a pre-trained deberta sequence classification model for the label sequence classification issue. the remainder of this paper is organized as follows: section 2 provides an overview of our proposed model and system. section 3 conducted the experiments to analyze the effectiveness of the proposed method. the paper concludes with a summary and reflections in section 4. ## system description this section delves into the architecture of the proposed model, detailing its essential components: the tokenizer, the pre-trained deberta model, and the implementation of regularized dropout and focal loss for neural networks. specifically, the model tailored for task 1, which addresses the multi-label sequence classification problem, is illustrated in figure 1 . meanwhile, the models designed for tasks 2 and 3, focusing on binary sequence classification issues, are depicted in figure 2 . ## conclusions this paper proposes a deep learning model for sentence sequence classification tasks, utilizing the deberta sentence sequence classification model as the foundation. achievements have been made in the final submission for semeval-2024 task10. however, there remains significant room for improvement in both the model and its parameters. therefore, in future studies, enhancements will be made to the model to achieve better results.