@InProceedings{xue-EtAl:2017:I17-2,
  author    = {Xue, Wei  and  Zhou, Wubai  and  Li, Tao  and  Wang, Qing},
  title     = {MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {151--156},
  abstract  = {Online reviews are valuable resources not only for consumers to make decisions
	before purchase, but also for providers to get feedbacks for their services or
	commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to
	identify aspect categories and extract aspect terms from the sentences of
	user-generated reviews. 
	However, the two tasks are often treated independently, even though they are
	closely related. Intuitively, the learned knowledge of one task should inform
	the other learning task. In this paper, we propose a multi-task learning model
	based on neural networks to solve them together. We demonstrate the improved
	performance of our multi-task learning model over the models trained separately
	on three public dataset released by SemEval workshops.},
  url       = {http://www.aclweb.org/anthology/I17-2026}
}