Abstract
Sentence Semantic Equivalence Identification (SSEI) plays a key role in the Retrieval-based Question Answering (rQA) systems. Nevertheless, for the resource limitation of many real applications, even the best SSEI models may underperform. To enhance the performance, this paper firstly proposes a novel deep neural network named Densely-connected Fusion Attentive Network (DFAN). The key idea behind our model is to learn the interactive semantic information with densely connection and fusion attentive mechanism. Secondly, for the limitation of the available corpus for the given domain, we add an auxiliary classification task, which categorizes questions into domain-specific classes. And pre-trained sentence embeddings learned from large unlabeled pairs are integrated as the weakly supervised learning strategy. We conduct experiments on datasets SNLI, Quora, and the domain corpus provided for a real rQA system, achieving competitive results on all. For the domain corpus, as the best F1 value of 93.29% reached by the proposed DFAN model with additional strategies, the measure hit@1 for the real rQA systems is 52.02%, which outperforms all compared methods. This result also shows that, getting satisfied performance for a real rQA system remains a challenging natural language processing task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
The result of BERT and MT-DNN in this dataset is 89.3% and 89.6%, as they used other data split of [25].
- 5.
We use the pre-trained model released by authors. There is only a base model in Chinese.
References
Aghaebrahimian, A.: Quora question answer dataset. In: Ekštein, K., Matoušek, V. (eds.) TSD 2017. LNCS (LNAI), vol. 10415, pp. 66–73. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64206-2_8
Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: International Conference on Learning Representations, ICLR 2017 (2017)
Bogdanova, D., dos Santos, C.N., Barbosa, L., Zadrozny, B.: Detecting semantically equivalent questions in online user forums. In: Proceedings of the 19th Conference on Computational Natural Language Learning, CoNLL 2015, Beijing, China, 30–31 July 2015, pp. 123–131 (2015)
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, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 632–642 (2015)
Britz, D., Goldie, A., Luong, M.T., Le, Q.: Massive exploration of neural machine translation architectures. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1442–1451 (2017)
Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading wikipedia to answer open-domain questions. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July – 4 August, vol. 1: Long Papers, pp. 1870–1879 (2017)
Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July – 4 August, vol. 1: Long Papers, pp. 1657–1668 (2017)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, 5–9 June 2008, pp. 160–167 (2008)
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 670–680 (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186 (2019)
Gong, Y., Bowman, S.R.: Ruminating reader: reasoning with gated multi-hop attention. In: Proceedings of the Workshop on Machine Reading for Question Answering@ACL 2018, Melbourne, Australia, 19 July 2018, pp. 1–11 (2018)
Gong, Y., Luo, H., Zhang, J.: Natural language inference over interaction space. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April – 3 May 2018, Conference Track Proceedings (2018). https://openreview.net/forum?id=r1dHXnH6-
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778 (2016)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8–13 December 2014, Montreal, Quebec, Canada, pp. 2042–2050 (2014)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 2261–2269 (2017)
Inkpen, D., Zhu, X., Ling, Z., Chen, Q., Wei, S.: Neural natural language inference models enhanced with external knowledge. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, vol. 1: Long Papers, pp. 2406–2417 (2018)
Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015, pp. 957–966 (2015)
Liu, X., He, P., Chen, W., Gao, J.: Multi-task deep neural networks for natural language understanding. CoRR abs/1901.11504 (2019)
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)
Parikh, A.P., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 2249–2255 (2016)
Pennington, J., Socher, R., Manning, C.D.: GloVe: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, a meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532–1543 (2014)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)
Tan, C., Wei, F., Wang, W., Lv, W., Zhou, M.: Multiway attention networks for modeling sentence pairs. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, 13–19 July 2018, Stockholm, Sweden, pp. 4411–4417 (2018)
Tay, Y., Luu, A.T., Hui, S.C.: Compare, compress and propagate: enhancing neural architectures with alignment factorization for natural language inference. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October – 4 November 2018, pp. 1565–1575 (2018)
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of the Workshop: Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2018, Brussels, Belgium, 1 November 2018, pp. 353–355 (2018)
Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 4144–4150 (2017)
Yin, W., Schütze, H., Xiang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. TACL 4, 259–272 (2016)
Yu, J., Qiu, M., Jiang, J., Huang, J., Song, S., Chu, W., Chen, H.: Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, 5–9 February 2018, pp. 682–690 (2018)
Zhang, X., Sun, X., Wang, H.: Duplicate question identification by integrating framenet with neural networks. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-2018), the 30th Innovative Applications of Artificial Intelligence (IAAI-2018), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-2018), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 6061–6068 (2018)
Acknowledgments
We would like to thank the anonymous reviewers and Li Gui and Fiona Liu for their helpful feedback. This work is supported by Natural Science Foundation of China (Grant No. 61872113), and the joint project foundation of Tencent Group.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, D., Chen, Q., Chen, S., Liu, X., Tang, B., Tan, B. (2019). Multi-strategies Method for Cold-Start Stage Question Matching of rQA Task. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-32233-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32232-8
Online ISBN: 978-3-030-32233-5
eBook Packages: Computer ScienceComputer Science (R0)
