Colloquium: Learning from Natural Language Supervision

Abstract: Humans can efficiently learn and communicate new knowledge about the world through natural language (e.g, the concept of important emails may be described through explanations like ‘late night emails from my boss are usually important’). Can machines be similarly taught new tasks and behavior through natural language interactions with their users? In this talk, we'll explore two approaches towards language-based learning for classifications tasks. First, we'll consider how language can be leveraged for interactive feature space construction for learning tasks. I'll present a method that jointly learns to understand language and learn classification models, by using explanations in conjunction with a small number of labeled examples of the concept. Secondly, we'll examine an approach for using language as a substitute for labeled supervision for training machine learning models, which leverages the semantics of quantifier expressions in everyday language (`definitely', `sometimes', etc.) to enable learning in scenarios with limited or no labeled data.