Abstract
Knowledge plays an essential role in inference, but is less explored by previous works in the Natural Language Inference (NLI) task. Although traditional neural models obtained impressive performance on standard benchmarks, they often encounter performance degradation when being applied to knowledge-intensive domains like medicine and science. To address this problem and further fill the knowledge gap, we present a simple Evidence-Based Inference Model (EBIM) to integrate clues collected from knowledge graphs as evidence for inference. To effectively incorporate the knowledge, we propose an efficient approach to retrieve paths in knowledge graphs as clues and then prune them to avoid involving too much irrelevant noise. In addition, we design a specialized CNN-based encoder according to the structure of clues to better model them. Experiments show that the proposed encoder outperforms strong baselines, and our EBIM model outperforms other knowledge-based approaches on the SciTail benchmark and establishes a new state-of-the-art performance on the MedNLI dataset.
This work is supported by Basic Research Funds for Higher Education Institution in Heilongjiang Province (Fundamental Research Project, Grant No.2020-KYYWF-1011).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Keeton, K., Roscoe, T. (eds.) 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, 2–4 November 2016, pp. 265–283. USENIX Association (2016). https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
Aronson, A.R., Lang, F.: An overview of metamap: historical perspective and recent advances. JAMIA 17(3), 229–236 (2010). https://doi.org/10.1136/jamia.2009.002733
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database-Issue), 267–270 (2004). https://doi.org/10.1093/nar/gkh061
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. TACL 5, 135–146 (2017). https://transacl.org/ojs/index.php/tacl/article/view/999
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Màrquez, L., Callison-Burch, C., Su, J., Pighin, D., Marton, Y. (eds.) Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 632–642. The Association for Computational Linguistics (2015). http://aclweb.org/anthology/D/D15/D15-1075.pdf
Chen, Q., Zhu, X., Ling, Z., Inkpen, D., Wei, S.: Neural natural language inference models enhanced with external knowledge. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, ACL 2018, Melbourne, Australia, 15–20 July 2018, pp. 2406–2417. Association for Computational Linguistics (2018). https://aclanthology.info/papers/P18-1224/p18-1224
Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Barzilay, R., Kan, M. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, ACL 2017, Vancouver, Canada, 30 July–4 August, pp. 1657–1668. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1152
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. In: Palmer, M., Hwa, R., Riedel, S. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 670–680. Association for Computational Linguistics (2017). https://aclanthology.info/papers/D17-1070/d17-1070
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018). http://arxiv.org/abs/1810.04805
Ganitkevitch, J., Durme, B.V., Callison-Burch, C.: PPDB: the paraphrase database. In: Vanderwende, L., III, H.D., Kirchhoff, K. (eds.) Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA, 9–14 June 2013, pp. 758–764. The Association for Computational Linguistics (2013). http://aclweb.org/anthology/N/N13/N13-1092.pdf
Guo, M., Zhang, Y., Liu, T.: Gaussian transformer: a lightweight approach for natural language inference. Proc. AAAI Conf. Artif. Intell. 33(01), 6489–6496 (July 2019). https://doi.org/10.1609/aaai.v33i01.33016489. https://ojs.aaai.org/index.php/AAAI/article/view/4614
Kang, D., Khot, T., Sabharwal, A., Clark, P.: Bridging knowledge gaps in neural entailment via symbolic models. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October–4 November 2018, pp. 4940–4945. Association for Computational Linguistics (2018). https://aclanthology.info/papers/D18-1535/d18-1535
Kang, D., Khot, T., Sabharwal, A., Hovy, E.H.: Adventure: adversarial training for textual entailment with knowledge-guided examples. In: Gurevych, I., Miyao, Y. (eds.) Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, ACL 2018, Melbourne, Australia, 15–20 July 2018, pp. 2418–2428. Association for Computational Linguistics (2018). https://aclanthology.info/papers/P18-1225/p18-1225
Khot, T., Sabharwal, A., Clark, P.: Scitail: a textual entailment dataset from science question answering. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), The 30th innovative Applications of Artificial Intelligence (IAAI-18), and The 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 5189–5197. AAAI Press (2018). https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17368
Loshchilov, I., Hutter, F.: Fixing weight decay regularization in Adam. CoRR abs/1711.05101 (2017). http://arxiv.org/abs/1711.05101
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748. http://doi.acm.org/10.1145/219717.219748
Mou, L., et al.: Natural language inference by tree-based convolution and heuristic matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Volume 2: Short Papers, ACL 2016, Berlin, Germany, 7–12 August 2016. The Association for Computer Linguistics (2016). http://aclweb.org/anthology/P/P16/P16-2022.pdf
Romanov, A., Shivade, C.: Lessons from natural language inference in the clinical domain. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October–4 November 2018, pp. 1586–1596. Association for Computational Linguistics (2018). https://aclanthology.info/papers/D18-1187/d18-1187
Speer, R., Havasi, C.: Representing general relational knowledge in conceptnet 5. In: Calzolari, N., et al. (eds.) Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012, Istanbul, Turkey, 23–25 May 2012, pp. 3679–3686. European Language Resources Association (ELRA) (2012). http://www.lrec-conf.org/proceedings/lrec2012/summaries/1072.html
Talman, A., Yli-Jyrä, A., Tiedemann, J.: Sentence embeddings in NLI with iterative refinement encoders. Nat. Lang. Eng. 25(4), 467–482 (2019)
Wang, X., et al.: Improving natural language inference using external knowledge in the science questions domain. CoRR abs/1809.05724 (2018). http://arxiv.org/abs/1809.05724
Williams, A., Nangia, N., Bowman, S.R.: A broad-coverage challenge corpus for sentence understanding through inference. In: Walker, M.A., Ji, H., Stent, A. (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), NAACL-HLT 2018, New Orleans, Louisiana, USA, 1–6 June 2018, pp. 1112–1122. Association for Computational Linguistics (2018). https://aclanthology.info/papers/N18-1101/n18-1101
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jia, B., Xu, H., Guo, M. (2021). Natural Language Inference Using Evidence from Knowledge Graphs. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_1
Download citation
DOI: https://doi.org/10.1007/978-981-16-5943-0_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5942-3
Online ISBN: 978-981-16-5943-0
eBook Packages: Computer ScienceComputer Science (R0)