@InProceedings{gui-EtAl:2017:EMNLP20171,
  author    = {Gui, Lin  and  Hu, Jiannan  and  He, Yulan  and  Xu, Ruifeng  and  Qin, Lu  and  Du, Jiachen},
  title     = {A Question Answering Approach for Emotion Cause Extraction},
  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     = {1593--1602},
  abstract  = {Emotion cause extraction aims to identify the reasons behind a certain emotion
	expressed in text. It is a much more difficult task compared to emotion
	classification. Inspired by recent advances in using deep memory networks for
	question answering (QA), we propose a new approach which considers emotion
	cause identification as a reading comprehension task in QA. Inspired by
	convolutional neural networks, we propose a new mechanism to store relevant
	context in different memory slots to model context information. Our proposed
	approach can extract both word level sequence features and lexical features.
	Performance evaluation shows that our method achieves the state-of-the-art
	performance on a recently released emotion cause dataset, outperforming a
	number of competitive baselines by at least 3.01% in F-measure.},
  url       = {https://www.aclweb.org/anthology/D17-1167}
}