Thesis Defense

Abstract:

Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples. We are interested in providing an alternative way of communicating knowledge to an automated system, by allowing a human teacher to interact with an automated learner using natural language instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. The process of learning a decision function is therefore viewed as a natural language lesson interpretation problem instead of learning from labeled examples. The lesson interpretation problem, framed as a structure prediction problem, is typically approached using supervised machine learning techniques which are often as costly and difficult as learning the original decision function.

I will discuss how to approach this learning problem without direct supervision, and present learning protocols which rely on indirect supervision originating from evaluating the learner's performance on the final concept taught. This learning scenario which relies on the connection between the two learning tasks, can be generally applied to other learning problems in natural language processing. I will also discuss how to model this problem more broadly, and discuss other issues in semantic interpretation such as the appropriate representation for learning and semantic knowledge transfer.