Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

  • Wei Li
  • Yunfang WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)


In this paper, we focus on the problem of answer triggering addressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep interactions between the candidate answers and the question. Our result on F value achieves 42.6%, which surpasses the baseline by over 10 %.


Answer Triggering Question Answering Hierarchical gated recurrent neural tensor network 



This work is supported by the National Key Basic Research Program of China (2014CB340504), the National Natural Science Foundation of China (61371129, 61572245).


  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Man´e, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Vi´egas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems. Software available from (2015).
  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  3. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  4. dos Santos, C.N., Tan, M., Xiang, B., Zhou, B.: Attentive pooling networks. CoRR, abs/1602.03609 (2016)Google Scholar
  5. Feng, M., Xiang, B., Glass, M.R., Wang, L., Zhou, B.: Applying deep learning to answer selection: a study and an open task. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, pp. 813–820 (2015)Google Scholar
  6. Heilman, M., Smith, N.A.: Tree edit models for recognizing textual entailments, paraphrases, and answers to questions. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 1011–1019 (2010)Google Scholar
  7. Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., Blunsom, P.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, pp. 1693–1701 (2015)Google Scholar
  8. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  9. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  10. Qiu, X., Huang, X.: Convolutional neural tensor network architecture for community based question answering. In: IJCAI, pp. 1305–1311 (2015)Google Scholar
  11. Severyn, A., Moschitti, A.: Automatic feature engineering for answer selection and extraction. In: EMNLP, vol. 13, pp. 458–467 (2013)Google Scholar
  12. Tan, M., dos Santos, C., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108 (2015)
  13. Wang, B., Liu, K., Zhao, J.: Inner attention based recurrent neural networks for answer selection. In: The Annual Meeting of the Association for Computational Linguistics (2016)Google Scholar
  14. Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: ACL, (2), pp. 707–712 (2015)Google Scholar
  15. Wang, M., Smith, N.A., Mitamura, T.: What is the jeopardy model? A quasi synchronous grammar for QA. In: EMNLP-CoNLL, vol. 7, pp. 22–32 (2007)Google Scholar
  16. Wang, S., Jiang, J.: A compare aggregate model for matching text sequences. arXiv preprint arXiv:1611.01747 (2016)
  17. Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. arXiv preprint arXiv:1702.03814 (2017)
  18. Yang, Y., Yih, W., Meek, C.: Wikiqa: a challenge dataset for open-domain question answering. In: EMNLP. Citeseer, pp. 2013–2018 (2015)Google Scholar
  19. Yao, X., Van Durme, B., Callison-Burch, C., Clark, P.: Answer extraction as sequence tagging with tree edit distance. In: HLTNAACL. Citeseer, pp. 858–867 (2013)Google Scholar
  20. Yin, W., Schütze, H., Xiang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. arXiv preprint arXiv:1512.05193 (2015)

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, School of Electronic Engineering and Computer SciencePeking UniversityBeijingChina

Personalised recommendations