@InProceedings{lee-EtAl:2017:EMNLP2017,
  author    = {Lee, Kenton  and  He, Luheng  and  Lewis, Mike  and  Zettlemoyer, Luke},
  title     = {End-to-end Neural Coreference Resolution},
  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     = {188--197},
  abstract  = {We introduce the first end-to-end coreference resolution model and show that it
	significantly outperforms all previous work without using a syntactic parser or
	hand-engineered mention detector. The key idea is to directly consider all
	spans in a document as potential mentions and learn distributions over possible
	antecedents for each. The model computes span embeddings that combine
	context-dependent boundary representations with a head-finding attention
	mechanism. It is trained to maximize the marginal likelihood of gold antecedent
	spans from coreference clusters and is factored to enable aggressive pruning of
	potential mentions. Experiments demonstrate state-of-the-art performance, with
	a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model
	ensemble, despite the fact that this is the first approach to be successfully
	trained with no external resources.},
  url       = {https://www.aclweb.org/anthology/D17-1018}
}