Acquiring and Understanding Cross-Task Generalization with Diverse NLP Tasks

Explore the concept of cross-task generalization in natural language processing through this informative lecture presented by Qinyuan Ye from USC Information Sciences Institute. Delve into the speaker's research on building learning environments for acquiring and evaluating cross-task generalization, including the creation of NLP Few-shot Gym and the CrossFit few-shot learning challenge. Discover insights from empirical analyses using multi-task learning and meta-learning approaches. Learn about task-level mixture-of-expert models developed to understand how models acquire transferable skills for cross-task generalization. Gain valuable knowledge on reducing human annotation efforts in NLP through distant supervision, high-level human supervision, and meta-learning techniques. Presented on October 6, 2022, this hour-long talk offers a comprehensive look at cutting-edge research in NLP and machine learning.

Acquiring and Understanding Cross-Task Generalization with Diverse NLP Tasks