2024 EACL EACL 2024

SarcEmp - Fine-tuning DialoGPT for Sarcasm and Empathy

Abstract

AbstractConversational models often face challenges such as a lack of emotional temperament and a limited sense of humor when interacting with users. To address these issues, we have selected relevant data and fine-tuned the model to (i) humanize the chatbot based on the user’s emotional response and the context of the conversation using a dataset based on empathy and (ii) enhanced conversations while incorporating humor/sarcasm for better user engagement. We aspire to achieve more personalized and enhanced user-computer interactions with the help of varied datasets involving sarcasm together with empathy on top of already available state-of-the-art conversational systems.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors