Learning strategies for story comprehension: a reinforcement learning approach

Authors:
Eugene Grois;David C. Wilkins
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

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Abstract

This paper describes the use of machine learning to improve the performance of natural language question answering systems. We present a model for improving story comprehension through inductive generalization and reinforcement learning, based on classified examples. In the process, the model selects the most relevant and useful pieces of lexical information to be used by the inference procedure. We compare our approach to three prior non-learning systems, and evaluate the conditions under which learning is effective. We demonstrate that a learning-based approach can improve upon "matching and extraction"-only techniques.