F eed F orward at S em E val-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations (2024)

Abstract: This paper reports on an innovative approach to Emotion Recognition in Conversation and Emotion Flip Reasoning for the SemEval-2024 competition with a specific focus on analyzing Hindi-English code-mixed language. By integrating Large Language Models (LLMs) with Instruction-based Fine-tuning and Quantized Low-Rank Adaptation (QLoRA), this study introduces innovative techniques like Sentext-height and advanced prompting strategies to navigate the intricacies of emotional analysis in code-mixed conversational data. The results of the proposed work effectively demonstrate its ability to overcome label bias and the complexities of code-mixed languages. Our team achieved ranks of 5, 3, and 3 in tasks 1, 2, and 3 respectively. This study contributes valuable insights and methods for enhancing emotion recognition models, underscoring the importance of continuous research in this field.

## introduction emotional analysis has come quite a long way. in the context of natural language processing (nlp), history reveals an evolution of the emotion analysis task. the task has always been about recognizing emotions from text, evolving from those early-day systems that were able to recognize emotions from standalone text @xcite @xcite to the current cutting-edge challenge of emotion recognition in conversation (erc) @xcite . well-designed simple methods have demonstrated that recognizing the emotion of a user's expression enables a broad range of practical applications in diverse domains, from e-commerce @xcite to healthcare @xcite . erc plays a significant role in illustrating how the emotion change during the interpersonal com-munications. by contrast to the isolation of single texts, erc struggles with how emotions shift through a combination of different speakers in conversation. motivated by the urgent need to understand the complex interactions of emotions during dialogue, a new issue has arisen-emotion-flip reasoning (efr) @xcite . efr is a novel endeavour aiming at identifying precisely which utterances transform an emotion within a person's flow of speech. apart from just emotions, efr seeks to unravel the complexities of emotion flips, offering valuable insights into the dynamics of human interaction. emotional flips can result from internal party interactions or from external elements such as speaker gestures or verbal messages. the practical importance of efr extends beyond theoretical limitations. in reality, it has applications in a variety of sectors. efr plays a crucial part in the development of reward and punishment systems, as well as interpretable emotion recognition systems. further, the widespread use of hindi-english code-mixed language online shows the cultural change. nlp is facing new challenges in the accurate identification of emotions in a dynamic cultural context. language switching during the conversation makes the work of emotion recognition systems even more complex. further building an adaptable system capable of capturing the subtle variations in emotions that emerge in such a hybrid language setting is the need of the hour. in order to promote research in this field, the organisers of semeval 2024, emotion discovery and reasoning its flip in conversation (ediref) foot_0 organised a shared task. the organisers created three sub-tasks: the following is an illustration of the definition for the erc and efr tasks: our team, feedforward, participated in semeval-2024 task 10 and achieved rankings of 5, 3, and 3 in subtasks 1, 2, and 3, respectively foot_1 . for detailed insights and findings regarding this task, please refer to the task description paper of semeval-2024 task 10 ( @xcite .to tackle this problem, we propose state-of-the-art techniques such as sentext-height for emotion recognition in multiparty conversations and ratio-wise splitting in trigger datasets for the efr task. additionally, we utilized instruction-based qlora training of 7billion-parameter models for both erc and efr tasks. the outline of the article is as follows: section 2 offers an in-depth exploration of the background study. in addition, section 4 comprehensively discusses the proposed methodologies. finally, the experimental outcomes are illustrated in section 5. ## related work emotion detection in the standalone text is a wellknown challenge in the natural language processing domain @xcite . however, unlike single text, emotion recognition in conversation data requires numerous complicated understandings of contextual information and speakers @xcite . in accordance with this, the majority of studies used deep neural networks with memory functions to solve sophisticated understandings of conversational text data @xcite . furthermore, the developers of @xcite attempt to include the role of speakers into the conversational model by using memory networks during two-party discussions. the utilization of external information is also vital in recognizing emotions in multi-party conversations. the authors of @xcite proposed the dimmn network for capturing speaker interaction information during multi-party conversations, in addition to text, audio, and video aspects during experiments. conventional categorical label-based approaches fail to capture quantitative measurements of emotion; to solve this issue, the authors of @xcite created a low-dimensional cluster-level contrastive learning model incorporating linguistic and factual information. furthermore, the @xcite established a discourse link between utterances by adding symbolic information into multi-party interactions. erc in low-resource code-mixed text has received little attention. the authors of @xcite created a hindi-english emotion-annotated corpus and established a transformer-based end-to-end framework with multitask learning. furthermore, most existing studies only account for emotion recognition, but very few studies looked beyond emotion recognition to interpret the results. in one such study, @xcite , the authors introduced a novel emotion-flip reasoning (efr), which aims to identify past utterances that have triggered one's emotional state to flip at a certain time, in addition to erc. ## results this section presents a comprehensive study on outcomes from task 1, task 2, and task 3. the weighted-f1 score is used as the standard metric for all tasks, as recommended by the task organizers and utilized to evaluate the submission hosted on codalab. ## conclusion this paper discusses the proposed work for the competition ediref semeval-2024 hosted on codalab. the study mainly focuses on emotion and emotion flip-trigger analysis specifically within multi-party conversational data. through innovative approaches and the utilization of state-ofthe-art techniques such as large language models (llms), instruction-based fine-tuning, and quantized low-rank adaptation (qlora), our team achieved promising results in emotion recognition in conversation (erc) and emotion flip reasoning (efr) tasks. however, obstacles persist, especially in addressing label bias and capturing nuanced emotions in hindi-english code-mixed language. the findings underscore the need for further research to enhance model performance, ultimately improving emotional analysis in conversational data. for the model to misclassify instances of these emotions as other classes. moreover, the overall accuracy of our model is moderate, indicating room for improvement in effectively distinguishing between the diverse emotional states. these findings emphasize the importance of addressing biases in the dataset and further fine-tuning the model to enhance its ability to accurately classify a wider range of emotions.