Advertisement

An Exploration of Dropout with RNNs for Natural Language Inference

  • Amit GajbhiyeEmail author
  • Sardar Jaf
  • Noura Al Moubayed
  • A. Stephen McGough
  • Steven Bradley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)

Abstract

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model. We also investigate the impact of varying dropout rates at these layers. Our empirical evaluation on a large (Stanford Natural Language Inference (SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward connection severely affects the model accuracy at increasing dropout rates. We also show that regularizing the embedding layer is efficient for SNLI whereas regularizing the recurrent layer improves the accuracy for SciTail. Our model achieved an accuracy \(86.14 \%\) on the SNLI dataset and \(77.05 \%\) on SciTail.

Keywords

Neural networks Dropout Natural Language Inference 

References

  1. 1.
    Bluche, T., Kermorvant, C., Louradour, J.: Where to apply dropout in recurrent neural networks for handwriting recognition? In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 681–685. IEEE (2015)Google Scholar
  2. 2.
    Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 632–642. Association for Computational Linguistics (2015)Google Scholar
  3. 3.
    Bowman, S.R., Gauthier, J., Rastogi, A., Gupta, R., Manning, C.D., Potts, C.: A fast unified model for parsing and sentence understanding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1466–1477 (2016)Google Scholar
  4. 4.
    Chen, Q., Zhu, X., Ling, Z.H., Inkpen, D.: Natural language inference with external knowledge. arXiv preprint arXiv:1711.04289 (2017)
  5. 5.
    Cheng, G., Peddinti, V., Povey, D., Manohar, V., Khudanpur, S., Yan, Y.: An exploration of dropout with LSTMs. In: Proceedings of Interspeech (2017)Google Scholar
  6. 6.
    Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 551–561 (2016)Google Scholar
  7. 7.
    Choi, J., Yoo, K.M., Lee, S.G.: Learning to Compose Task-specific Tree Structures. AAAI (2017)Google Scholar
  8. 8.
    Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019–1027 (2016)Google Scholar
  9. 9.
    Ghaeini, R., et al.: Dr-bilstm: Dependent reading bidirectional LSTM for natural language inference. arXiv preprint arXiv:1802.05577 (2018)
  10. 10.
    Khot, T., Sabharwal, A., Clark, P.: SciTail: a textual entailment dataset from science question answering. In: Proceedings of AAAI (2018)Google Scholar
  11. 11.
    Kim, Y., Denton, C., Hoang, L., Rush, A.M.: Neural machine translation by jointly learning to align and translate. In: Proceedings of ICLR (2017)Google Scholar
  12. 12.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  13. 13.
    Liu, Y., Sun, C., Lin, L., Wang, X.: Learning natural language inference using bidirectional LSTM model and inner-attention. CoRR abs/1605.09090 (2016)Google Scholar
  14. 14.
    MacCartney, B.: Natural language inference. Stanford University (2009)Google Scholar
  15. 15.
    Munkhdalai, T., Yu, H.: Neural tree indexers for text understanding. In: Proceedings of the Conference, Association for Computational Linguistics, Meeting, vol. 1, p. 11. NIH Public Access (2017)Google Scholar
  16. 16.
    Pachitariu, M., Sahani, M.: Regularization and nonlinearities for neural language models: when are they needed? arXiv preprint arXiv:1301.5650 (2013)
  17. 17.
    Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  18. 18.
    Pham, V., Bluche, T., Kermorvant, C., Louradour, J.: Dropout improves recurrent neural networks for handwriting recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 285–290. IEEE (2014)Google Scholar
  19. 19.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Tay, Y., Tuan, L.A., Hui, S.C.: A compare-propagate architecture with alignment factorization for natural language inference. arXiv preprint arXiv:1801.00102 (2017)
  21. 21.
    Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Amit Gajbhiye
    • 1
    Email author
  • Sardar Jaf
    • 1
  • Noura Al Moubayed
    • 1
  • A. Stephen McGough
    • 2
  • Steven Bradley
    • 1
  1. 1.Department of Computer ScienceDurham UniversityDurhamUK
  2. 2.School of ComputingNewcastle UniversityNewcastle upon TyneUK

Personalised recommendations