@InProceedings{yin-song-zhang:2017:EMNLP2017,
  author    = {Yin, Yichun  and  Song, Yangqiu  and  Zhang, Ming},
  title     = {Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension},
  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     = {2044--2054},
  abstract  = {Document-level multi-aspect sentiment classification is an important task for
	customer relation management. In this paper, we model the task as a machine
	comprehension problem where pseudo question-answer pairs are constructed by a
	small number of aspect-related keywords and aspect ratings. A hierarchical
	iterative attention model is introduced to build aspectspecific representations
	by frequent and repeated interactions between documents and aspect questions.
	We adopt a hierarchical architecture to represent both word level and sentence
	level information, and use the attention operations for aspect questions and
	documents alternatively with the multiple hop mechanism. Experimental results
	on the TripAdvisor and BeerAdvocate datasets show that our model outperforms
	classical baselines. We will release our code and data for the method
	replicability.},
  url       = {https://www.aclweb.org/anthology/D17-1217}
}