@InProceedings{ouchi-shindo-matsumoto:2017:Long,
  author    = {Ouchi, Hiroki  and  Shindo, Hiroyuki  and  Matsumoto, Yuji},
  title     = {Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1591--1600},
  abstract  = {The performance of Japanese predicate argument structure (PAS) analysis has
	improved in recent years thanks to the joint modeling of interactions between
	multiple predicates. However, this approach relies heavily on syntactic
	information predicted by parsers, and suffers from errorpropagation. To remedy
	this problem, we
	introduce a model that uses grid-type recurrent neural networks. The proposed
	model automatically induces features sensitive to multi-predicate interactions
	from
	the word sequence information of a sentence. Experiments on the NAIST Text
	Corpus demonstrate that without syntactic information, our model outperforms
	previous syntax-dependent models.},
  url       = {http://aclweb.org/anthology/P17-1146}
}