@InProceedings{kabbara-feng-cheung:2016:COLING,
  author    = {Kabbara, Jad  and  Feng, Yulan  and  Cheung, Jackie Chi Kit},
  title     = {Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2625--2634},
  abstract  = {We examine the potential of recurrent neural networks for handling pragmatic
	inferences involving complex contextual cues for the task of article usage
	prediction. We train and compare several variants of Long Short-Term Memory
	(LSTM) networks with an attention mechanism. Our model outperforms a previous
	state-of-the-art system, achieving up to 96.63% accuracy on the WSJ/PTB corpus.
	In addition, we perform a series of analyses to understand the impact of
	various model choices. We find that the gain in performance can be attributed
	to the ability of LSTMs to pick up on contextual cues, both local and further
	away in distance, and that the model is able to solve cases involving reasoning
	about coreference and synonymy. We also show how the attention mechanism
	contributes to the interpretability of the model's effectiveness.},
  url       = {http://aclweb.org/anthology/C16-1247}
}