Faculty Recruiting Support CICS

Machine Learning and Friends Lunch (Online)

16 Sep
Thursday, 09/16/2021 12:00pm to 1:00pm
Virtual via Zoom
Machine Learning and Friends Lunch
Speaker: Trapit Bansal

Title: "Few-Shot Natural Language Processing via Self-Supervised Meta-Learning"

Abstract: Humans show a remarkable capability to accurately solve a wide range of problems efficiently -- utilizing a limited amount of computation and experience. Deep learning models, by stark contrast, can be trained to be highly accurate on a narrow task while being highly inefficient in terms of the amount of compute and data required to reach that accuracy. Few-shot learning considers this problem of learning models that generalize to new tasks with very little supervision. Natural language processing (NLP) has seen recent breakthroughs with unsupervised pre-training of large models that can be applied to many NLP tasks, however, few-shot learning of new tasks is still inefficient. In this talk, I will present a sequence of work on meta-learning for improving few-shot learning of NLP tasks. Meta-learning, or learning to learn, treats the learning process itself as a learning problem from data, to learn systems that can generalize to new tasks efficiently. However, meta-learning requires a distribution over tasks with relevant labeled data that can be difficult to obtain, severely limiting the practical utility of meta-learning methods. I will present solutions that construct task distributions from unlabeled text data to enable large-scale meta-learning. The resulting self-supervised meta-learning methods optimize the pre-training directly for future fine-tuning with few examples, which leads to improved few-shot learning of new tasks. By providing useful training tasks for meta-learning, these approaches help lift a pertinent bottleneck for training meta-learning methods and should enable many future applications of meta-learning in NLP, such as hyper-parameter optimization, continual learning, neural architecture search, and more.

Bio: Trapit is a Ph.D. student advised by Prof. Andrew McCallum at UMass Amherst. His recent research focuses on improving the generalization of natural language processing models with limited human-labeled data through meta-learning, self-supervised learning, and multi-task learning. In the past, he has also worked on machine learning methods for recommendation systems, information extraction, knowledge representation, and reinforcement learning for multi-agent systems. During his Ph.D., he has interned at Facebook, OpenAI, Google Research, and Microsoft Research. His work has also received a best paper award at ICLR 2018. Before starting his Ph.D., he obtained a B.S. and M.S. in Mathematics from the Indian Institute of Technology, Kanpur.

To obtain the Zoom link for this event, please see the event announcements from MLFL on the college email lists or contact Kalpesh Krishna.