@InProceedings{long-EtAl:2017:EMNLP20172,
  author    = {Long, Teng  and  Bengio, Emmanuel  and  Lowe, Ryan  and  Cheung, Jackie Chi Kit  and  Precup, Doina},
  title     = {World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {825--834},
  abstract  = {Humans interpret texts with respect to some background information, or world
	knowledge, and we would like to develop automatic reading comprehension systems
	that can do the same. In this paper, we introduce a task and several models to
	drive progress towards this goal. In particular, we propose the task of rare
	entity prediction: given a web document with several entities removed, models
	are tasked with predicting the correct missing entities conditioned on the
	document context and the lexical resources. This task is challenging due to the
	diversity of language styles and the extremely large number of rare entities.
	We propose two recurrent neural network architectures which make use of
	external knowledge in the form of entity descriptions. Our experiments show
	that our hierarchical LSTM model performs significantly better at the rare
	entity prediction task than those that do not make use of external resources.},
  url       = {https://www.aclweb.org/anthology/D17-1086}
}