@InProceedings{xiang-EtAl:2016:COLING,
  author    = {Xiang, Yang  and  Zhou, Xiaoqiang  and  Chen, Qingcai  and  Zheng, Zhihui  and  Tang, Buzhou  and  Wang, Xiaolong  and  Qin, Yang},
  title     = {Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1231--1241},
  abstract  = {In community question answering (cQA), the quality of answers are determined by
	the matching degree between question-answer pairs and the correlation among the
	answers. In this paper, we show that the dependency between the answer quality
	labels also plays a pivotal role. To validate the effectiveness of label
	dependency, we propose two neural network-based models, with different
	combination modes of Convolutional Neural Net-works, Long Short Term Memory and
	Conditional Random Fields. Extensive experi-ments are taken on the dataset
	released by the SemEval-2015 cQA shared task. The first model is a stacked
	ensemble of the networks. It achieves 58.96% on macro averaged F1, which
	improves the state-of-the-art neural network-based method by 2.82% and
	outper-forms the Top-1 system in the shared task by 1.77%. The second is a
	simple attention-based model whose input is the connection of the question and
	its corresponding answers. It produces promising results with 58.29% on overall
	F1 and gains the best performance on the Good and Bad categories.},
  url       = {http://aclweb.org/anthology/C16-1117}
}