Research Project Name

Learning Strategies for Story Comprehension: A Reinforcement Learning Approach


The objective is to construct a computational model that can answer Who/What/Why/When/How questions about a natural language story. The scientific goal is to achieve this by having all knowledge in the computational model be learned, using statistical and machine learning techniques, with no use of manually entered knowledge.


A computational model, called QABLEe, has been implemented. The initial lexical knowledge is constructed using off the shelf statistical language preprocessors over a subset of the target story corpus. Then the performance is improved using inductive generalization and reinforcement learning over classified examples to select the most useful lexical pieces of information to be used by the inference procedure when answering questions about the story. Finally, the model is tested on a subset of stories that have not been previously seen.

QABLe is compared to three prior non-learning systems on the Remedia corpus of fourth grade stories. The conditions under which learning is effective are evaluated. The experimental results show that a learning-based approach significantly improves upon “matching and extraction only” techniques.