@InProceedings{ghaeini-EtAl:2018:N18-1,
  author    = {Ghaeini, Reza  and  Hasan, Sadid A.  and  Datla, Vivek  and  Liu, Joey  and  Lee, Kathy  and  Qadir, Ashequl  and  Ling, Yuan  and  Prakash, Aaditya  and  Fern, Xiaoli  and  Farri, Oladimeji},
  title     = {DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1460--1469},
  abstract  = {We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.},
  url       = {http://www.aclweb.org/anthology/N18-1132}
}