6 citations · 6 across the 4 of their papers we have counts for
4 papers
Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis
Yaping Chai, Haoran Xie, Joe S. Qin
Implicit sentiment analysis is challenging because sentiment toward an aspect is often inferred from events rather than expressed through explicit opinion words. Existing models ty…
Emotion-Enhanced Multi-Task Learning with LLMs for Aspect Category Sentiment Analysis
Yaping Chai, Haoran Xie, Joe S. Qin
Aspect category sentiment analysis (ACSA) has achieved remarkable progress with large language models (LLMs), yet existing approaches primarily emphasize sentiment polarity while o…
Semantic-preserved Augmentation with Confidence-weighted Fine-tuning for Aspect Category Sentiment Analysis
Yaping Chai, Haoran Xie, Joe S. Qin
Large language model (LLM) is an effective approach to addressing data scarcity in low-resource scenarios. Recent existing research designs hand-crafted prompts to guide LLM for da…
Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities
Yaping Chai, Haoran Xie, Joe S. Qin
The increasing size and complexity of pre-trained language models have demonstrated superior performance in many applications, but they usually require large training datasets to b…