@InProceedings{marasovic-EtAl:2017:EMNLP2017,
  author    = {Marasovic, Ana  and  Born, Leo  and  Opitz, Juri  and  Frank, Anette},
  title     = {A Mention-Ranking Model for Abstract Anaphora 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     = {221--232},
  abstract  = {Resolving abstract anaphora is an important, but difficult task for text
	understanding. Yet, with recent advances in representation learning this task
	becomes a more tangible aim. A central property of abstract anaphora is that it
	establishes a relation between the anaphor embedded in the anaphoric sentence
	and its (typically non-nominal) antecedent. We propose a mention-ranking model
	that learns how abstract anaphors relate to their antecedents with an
	LSTM-Siamese Net. We overcome the lack of training data by generating
	artificial anaphoric sentence--antecedent pairs. Our model outperforms
	state-of-the-art results on shell noun resolution. We  also report first
	benchmark results on an abstract anaphora subset of the ARRAU corpus. This
	corpus presents a greater challenge due to a mixture of nominal and pronominal
	anaphors and a greater range of confounders. We found model variants that
	outperform the baselines for nominal anaphors, without training on individual
	anaphor data, but still lag behind for pronominal anaphors. Our model selects
	syntactically plausible candidates and -- if disregarding syntax --
	discriminates candidates using deeper features.},
  url       = {https://www.aclweb.org/anthology/D17-1021}
}