UCSC NLP at S em E val-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation ( ED i R e F ) (2024)

Abstract: We describe SemEval-2024 Task 10: EDiReF consisting of three sub-tasks involving emotion in conversation across Hinglish code-mixed and English datasets. Subtasks include classification of speaker emotion in multiparty conversations (Emotion Recognition in Conversation) and reasoning around shifts in speaker emotion state (Emotion Flip Reasoning). We deployed a BERT model for emotion recognition and two GRU-based models for emotion flip. Our model achieved F1 scores of 0.45, 0.79, and 0.68 for subtasks 1, 2, and 3, respectively.

## introduction emotion recognition in natural language provides quantifiable insights into the traditionally qualitative realm of emotive language, bridging fields such as psychology, cognition, and linguistics. the explosion of textual data in recent years from social media platforms like twitter and the introduction of highly cable text-processing models has provided researchers the opportunity to perform analyses on conversations that are highly complex. despite these developments, the inherent subjectivity of emotion continues to present a challenge to the field. without visual information and speech audio, nlp systems must decipher rapid changes in emotional states solely through text, missing out on the nuanced non-verbal cues that often signal these shifts during spoken interactions. the absence of these cues can lead to model inaccuracies during conversational transitions such as from joy to sarcasm, or from calmness to anger. understanding the dynamics of emotion in the context of conversations is vital for building better conversational agents. while classifying changes in the emotion of a speaker is an important first step in this goal, it comes up short of being able to explain why the change occurred. emotion flip reasoning is a task which has been proposed which seeks to identify the specific cause of speaker emotion flips in the context of a conversation @xcite . for example if a speaker's emotion in one utterance is joy but in their next utterance it is sad, we would like to pinpoint which utterances in the conversation caused it whether it be another speaker's or their own. ## system overview in this study, we introduce an integrated framework that combines bidirectional encoder representations from transformers (bert) with gated recurrent unit (gru) networks. ## conclusion the baseline approach, which employs basic embeddings of utterances and emotions, proved adequate for capturing emotion flip reasoning in large datasets, despite cultural differences and codemixing ambiguities. the need to pass conversational information is not a substantial indicator of the prediction efr triggers nor does passing the emotion labels into the embeddings. in the future, we plan to explore multitask classification with bert to determine if combined training enhances the transformer's ability to learn emotional sequence information or integrate additional conversational context to account for speaker dependencies, similar to emoberta's approach. we may attempt to replicate emoberta's methodology to see how much emotion labels increase efr accuracy. addressing the challenges of code-mixing in hinglish and enhancing cross-cultural emotion comprehension remain critical for improving the recognition of emotional transitions. saif mohammad and peter turney. 2010. emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. in proceedings of the naacl hlt 2010 workshop on computational approaches to analysis and generation of emotion in text, pages 26-34, los angeles, ca. association for computational linguistics.