Scripts encode knowledge of prototypical sequences of events. We describe a Recurrent Neural Network model for statistical script learning using Long Short-Term Memory, an architecture which has been demonstrated to work well on a range of Artificial Intelligence tasks. We evaluate our system on two tasks, inferring held-out events from text and inferring novel events from text, substantially outperforming prior approaches on both tasks.

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In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, February 2016.

@inproceedings{pichotta:aaai16, title={Learning Statistical Scripts with LSTM Recurrent Neural Networks}, author={Karl Pichotta and Raymond J. Mooney}, booktitle={Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16)}, month={February}, address={Phoenix, Arizona}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127543", year={2016} }

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