@InProceedings{raganato-dellibovi-navigli:2017:EMNLP2017,
  author    = {Raganato, Alessandro  and  {Delli Bovi}, Claudio  and  Navigli, Roberto},
  title     = {{Neural Sequence Learning Models for Word Sense Disambiguation}},
  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     = {1167--1178},
  abstract  = {Word Sense Disambiguation models exist in many flavors. Even though supervised
	ones tend to perform best in terms of accuracy, they often lose ground to more
	flexible knowledge-based solutions, which do not require training by a word
	expert for every disambiguation target. To bridge this gap we adopt a different
	perspective and rely on sequence learning to frame the disambiguation problem:
	we propose and study in depth a series of end-to-end neural architectures
	directly tailored to the task, from bidirectional Long Short-Term Memory to
	encoder-decoder models. Our extensive evaluation over standard benchmarks and
	in multiple languages shows that sequence learning enables more versatile
	all-words models that consistently lead to state-of-the-art results, even
	against word experts with engineered features.},
  url       = {https://www.aclweb.org/anthology/D17-1121}
}