Learning Strategies for Open-Domain Natural Language Question Answering

IJCAI 2005 pp. 1054-1060

doi:10.3115/1628960.1628977 /ijcai/2005/grois2005ijcai-learning/

Abstract

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.

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Text

Grois and Wilkins. "Learning Strategies for Open-Domain Natural Language Question Answering." International Joint Conference on Artificial Intelligence, 2005. doi:10.3115/1628960.1628977

Markdown

[Grois and Wilkins. "Learning Strategies for Open-Domain Natural Language Question Answering." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/grois2005ijcai-learning/) doi:10.3115/1628960.1628977

BibTeX

@inproceedings{grois2005ijcai-learning,
  title     = {{Learning Strategies for Open-Domain Natural Language Question Answering}},
  author    = {Grois, Eugene and Wilkins, David C.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {1054-1060},
  doi       = {10.3115/1628960.1628977},
  url       = {https://mlanthology.org/ijcai/2005/grois2005ijcai-learning/}
}