CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages
By: Jiyu Chen , Necva Bölücü , Sarvnaz Karimi and more
Potential Business Impact:
Reads feelings in writing, even in other languages.
Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The \textit{Semeval 2025 Task 11: Bridging the Gap in Text-Based emotion} shared task was organised to investigate emotion recognition across different languages. The goal of the task is to implement an emotion recogniser that can identify the basic emotional states that general third-party observers would attribute to an author based on their written text snippet, along with the intensity of those emotions. We report our investigation of various task-adaptation strategies for LLMs in emotion recognition. We show that the most effective method for this task is to fine-tune a pre-trained multilingual LLM with LoRA setting separately for each language.
Similar Papers
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Computation and Language
Helps computers understand feelings in many languages.
EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition
Audio and Speech Processing
Helps computers understand your feelings from your voice.
CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding
Computation and Language
Helps computers understand emotions across cultures.