@InProceedings{laha-raykar:2016:COLING,
  author    = {Laha, Anirban  and  Raykar, Vikas},
  title     = {An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks},
  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     = {2762--2773},
  abstract  = {Several tasks in argumentation mining and debating, question-answering, and
	natural language inference involve classifying a sequence in the context of
	another sequence (referred as bi-sequence classification). For several single
	sequence classification tasks, the current state-of-the-art approaches are
	based on recurrent and convolutional neural networks. On the other hand, for
	bi-sequence classification problems, there is not much understanding as to the
	best deep learning architecture. In this paper, we attempt to get an
	understanding of this category of problems by extensive empirical evaluation of
	19 different deep learning architectures (specifically on different ways of
	handling context) for various problems originating in natural language
	processing like debating, textual entailment and question-answering. Following
	the empirical evaluation, we offer our insights and conclusions regarding the
	architectures we have considered. We also establish the first deep learning
	baselines for three argumentation mining tasks.},
  url       = {http://aclweb.org/anthology/C16-1260}
}