@InProceedings{xia-EtAl:2017:Long,
  author    = {Xia, Qiaolin  and  Sha, Lei  and  Chang, Baobao  and  Sui, Zhifang},
  title     = {A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data},
  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     = {2069--2077},
  abstract  = {Previous studies on Chinese semantic role labeling (SRL) have concentrated on a
	single semantically annotated corpus. But the training data of single corpus is
	often limited. Whereas the other existing semantically annotated corpora for
	Chinese SRL are scattered across different annotation frameworks. But still,
	Data sparsity remains a bottleneck. This situation calls for larger training
	datasets, or effective approaches which can take advantage of highly
	heterogeneous data. In this paper, we focus mainly on the latter, that is, to
	improve Chinese SRL by using heterogeneous corpora together. We propose a novel
	progressive learning model which augments the Progressive Neural Network with
	Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and
	effectively transfer knowledge between them. We also release a new corpus,
	Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that our model
	outperforms state-of-the-art methods.},
  url       = {http://aclweb.org/anthology/P17-1189}
}