Paper: Learning Strategies For Open-Domain Natural Language Question Answering

ACL ID P05-2015
Title Learning Strategies For Open-Domain Natural Language Question Answering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors
  • Eugene Grois (University of Illinois at Urbana-Champaign, Urbana IL)

This work presents a model for learning inference procedures for story comprehension through inductive generalization and reinforcement learning, based on classified examples. The learned inference procedures (or strategies) are represented as of sequences of transformation rules. The approach is compared to three prior systems, and experimental results are presented demonstrating the efficacy of the model.