Abstract
Neural language representation models such as BERT, pre-trained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Language Inference (NLI) is a challenging reasoning task that relies on common human understanding of language and real-world commonsense knowledge. We introduce a new model for NLI called External Knowledge Enhanced BERT (ExBERT), to enrich the contextual representation with real-world commonsense knowledge from external knowledge sources and enhance BERT’s language understanding and reasoning capabilities. ExBERT takes full advantage of contextual word representations obtained from BERT and employs them to retrieve relevant external knowledge from knowledge graphs and to encode the retrieved external knowledge. Our model adaptively incorporates the external knowledge context required for reasoning over the inputs. Extensive experiments on the challenging SciTail and SNLI benchmarks demonstrate the effectiveness of ExBERT: in comparison to the previous state-of-the-art, we obtain an accuracy of \(95.9\%\) on SciTail and \(91.5\%\) on SNLI.
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We expect further improvements in ExBERT’s performance with \(\mathrm {BERT_{LARGE}}\), however we left the evaluation for future work due to the limited computing resources.
References
Bast, H., Björn, B., Haussmann, E.: Semantic search on text and knowledge bases. Found. Trends Inf. Retrieval 10(2–3), 119–271 (2016)
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: ACL (2015)
Chen, Q., Zhu, X., Ling, Z.H., Inkpen, D., Wei, S.: Neural natural language inference models enhanced with external knowledge. In: ACL (2018)
Dagan, I., Glickman, O., Magnini, B.: The PASCAL recognising textual entailment challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006). https://doi.org/10.1007/11736790_9
Dalvi, M.B., Tandon, N., Clark, P.: Domain-targeted, high precision knowledge extraction. Trans. Assoc. Comput. Linguist. 5, 233–246 (2017). https://www.transacl.org/ojs/index.php/tacl/article/view/1064
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the NAACL-HLT 2019 (Long and Short Papers), vol. 1, pp. 4171–4186 (2019)
Gajbhiye, A., Jaf, S., Moubayed, N.A., Bradley, S., McGough, A.S.: Cam: a combined attention model for natural language inference. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1009–1014, December 2018
Gajbhiye, A., Jaf, S., Moubayed, N.A., McGough, A.S., Bradley, S.: An exploration of dropout with RNNs for natural language inference. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 157–167. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_16
Gajbhiye, A., Winterbottom, T., Al Moubayed, N., Bradley, S.: Bilinear fusion of commonsense knowledge with attention-based NLI models. In: Farkaš, I., Masulli, P., Wermter, S. (eds.) ICANN 2020. LNCS, vol. 12396, pp. 633–646. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61609-0_50
Kang, D., Khot, T., Sabharwal, A., Hovy, E.: AdvEntuRe: adversarial training for textual entailment with knowledge-guided examples. In: ACL, Melbourne, July 2018
Kapanipathi, P., et al.: Infusing knowledge into the textual entailment task using graph convolutional networks. arXiv preprint arXiv:1911.02060 (2019)
Khot, T., Sabharwal, A., Clark, P.: Scitail: A textual entailment dataset from science question answering. In: AAAI, New Orleans, 2–7, February 2018
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) ICLR (2015)
Kwon, S., Kang, C., Han, J., Choi, J.: Why do masked neural language models still need common sense knowledge?. CoRR abs/1911.03024 (2019)
Li, A.H., Sethy, A.: Knowledge enhanced attention for robust natural language inference. arXiv preprint arXiv:1909.00102 (2019)
Liu, X., He, P., Chen, W., Gao, J.: Multi-task deep neural networks for natural language understanding. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4487–4496, Florence, July 2019
Logan, R., Liu, N.F., Peters, M.E., Gardner, M., Singh, S.: Barack’s wife hillary: using knowledge graphs for fact-aware language modeling. In: Proceedings of the 57th ACL, pp. 5962–5971, Florence, July 2019
Pang, D., Lin, L.H., Smith, N.A.: Improving natural language inference with a pretrained parser. arXiv preprint arXiv:1909.08217 (2019)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)
Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: An open multilingual graph of general knowledge (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 6000–6010 (2017)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017). https://doi.org/10.1109/TKDE.2017.2754499
Wang, X., et al.: Improving natural language inference using external knowledge in the science questions domain. In: Proceedings of the AAAI, vol. 33, pp. 7208–7215 (2019)
Yang, B., Mitchell, T.: Leveraging knowledge bases in LSTMs for improving machine reading. In: ACL, pp. 1436–1446, Vancouver, July 2017
Zhang, Z., et al.: Semantics-aware BERT for language understanding. ArXiv arXiv:1909.02209 (2020)
Zhang, Z., Wu, Y., Li, Z., Zhao, H.: Explicit contextual semantics for text comprehension. CoRR abs/1809.02794 http://arxiv.org/abs/1809.02794 (2018)
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Gajbhiye, A., Moubayed, N.A., Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12895. Springer, Cham. https://doi.org/10.1007/978-3-030-86383-8_37
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