Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requir- ing a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner us- ing natural 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 learn- ing process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem instead of learning from labeled examples. This interpretation of ma- chine learning is motivated by human learning pro- cesses, in which the learner is given a lesson de- scribing the target concept directly, and a few in- stances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that gets feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. This approach alleviates the supervision burden of traditional machine learn- ing by focusing on supplying the learner with only human-level task expertise for learning. We evaluate our approach by applying it to the rules of the Freecell solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also general- izes to previously unseen instructions.

Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underly- ing representation. This paper proposes to acquire representations for machine learn- ing by reading text written to accommo- date human learning. We propose a novel form of semantic analysis called read- ing to learn, where the goal is to obtain a high-level semantic abstract of multi- ple documents in a representation that fa- cilitates learning. We obtain this abstract through a generative model that requires no labeled data, instead leveraging repe- tition across multiple documents. The se- mantic abstract is converted into a trans- formed feature space for learning, result- ing in improved generalization on a rela- tional learning task.