Abstract
Zero-shot relation extraction is an important method for dealing with the newly emerging relations in the real world which lacks labeled data. However, the mainstream two-tower zero-shot methods usually rely on large-scale and in-domain labeled data of predefined relations. In this work, we view zero-shot relation extraction as a semantic matching task optimized by prompt-tuning, which still maintains superior generalization performance when the labeled data of predefined relations are extremely scarce. To maximize the efficiency of data exploitation, instead of directly fine-tuning, we introduce a prompt-tuning technique to elicit the existing relational knowledge in pre-trained language model (PLMs). In addition, very few relation descriptions are exposed to the model during training, which we argue is the performance bottleneck of two-tower methods. To break through the bottleneck, we model the semantic interaction between relational instances and their descriptions directly during encoding. Experiment results on two academic datasets show that (1) our method outperforms the previous state-of-the-art method by a large margin with different samples of predefined relations; (2) this advantage will be further amplified in the low-resource scenario.
J. Zhao and Y. Hu—Equal contribution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, C.Y., Li, C.T.: Zs-bert: towards zero-shot relation extraction with attribute representation learning. arXiv preprint arXiv:2104.04697 (2021)
Chen, Q., Zhu, X., Ling, Z.H., 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 (Volume 1: Long Papers), pp. 1657–1668. Association for Computational Linguistics, Vancouver, Canada (2017). https://doi.org/10.18653/v1/P17-1152, https://aclanthology.org/P17-1152
Clark, K., Khandelwal, U., Levy, O., Manning, C.D.: What does BERT look at? an analysis of bert’s attention. CoRR abs/1906.04341 (2019). http://arxiv.org/abs/1906.04341
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
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, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423
Ding, N., et al.: Prompt-learning for fine-grained entity typing. arXiv preprint arXiv:2108.10604 (2021)
Du, J., Han, J., Way, A., Wan, D.: Multi-level structured self-attentions for distantly supervised relation extraction. arXiv preprint arXiv:1809.00699 (2018)
Goldberg, Y.: Assessing bert’s syntactic abilities. CoRR abs/1901.05287 (2019). http://arxiv.org/abs/1901.05287
Han, X., Zhao, W., Ding, N., Liu, Z., Sun, M.: Ptr: prompt tuning with rules for text classification. arXiv preprint arXiv:2105.11259 (2021)
Han, X., et al.: Fewrel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4803–4809 (2018)
Han, X., Zhu, H., Yu, P., Wang, Z., Yao, Y., Liu, Z., Sun, M.: FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4803–4809. Association for Computational Linguistics, Brussels, Belgium (2018). https://doi.org/10.18653/v1/D18-1514, https://aclanthology.org/D18-1514
Hewitt, J., Manning, C.D.: A structural probe for finding syntax in word representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4129–4138. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1419, https://aclanthology.org/N19-1419
Jawahar, G., Sagot, B., Seddah, D.: What does BERT learn about the structure of language? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3651–3657. Association for Computational Linguistics, Florence, Italy (2019). https://doi.org/10.18653/v1/P19-1356, https://aclanthology.org/P19-1356
Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know? Trans. Assoc. Comput. Linguist. 8, 423–438 (2020). https://doi.org/10.1162/tacl_a_00324, https://aclanthology.org/2020.tacl-1.28
Levy, O., Seo, M., Choi, E., Zettlemoyer, L.: Zero-shot relation extraction via reading comprehension. In: Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pp. 333–342. Association for Computational Linguistics, Vancouver, Canada (2017). https://doi.org/10.18653/v1/K17-1034, https://aclanthology.org/K17-1034
Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (2021)
Madotto, A., Wu, C.S., Fung, P.: Mem2Seq: effectively incorporating knowledge bases into end-to-end task-oriented dialog systems. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1468–1478. Association for Computational Linguistics, Melbourne, Australia (2018). https://doi.org/10.18653/v1/P18-1136, https://aclanthology.org/P18-1136
Obamuyide, A., Vlachos, A.: Zero-shot relation classification as textual entailment. In: Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pp. 72–78. Association for Computational Linguistics, Brussels, Belgium (2018). https://doi.org/10.18653/v1/W18-5511, https://aclanthology.org/W18-5511
Peters, M.E., Neumann, M., Zettlemoyer, L., Yih, W.t.: Dissecting contextual word embeddings: architecture and representation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1499–1509. Association for Computational Linguistics, Brussels, Belgium (2018). https://doi.org/10.18653/v1/D18-1179, https://aclanthology.org/D18-1179
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)
Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: distributional similarity for relation learning. arXiv preprint arXiv:1906.03158 (2019)
Tenney, I., et al.: What do you learn from context? probing for sentence structure in contextualized word representations. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=SJzSgnRcKX
Tsimpoukelli, M., Menick, J., Cabi, S., Eslami, S., Vinyals, O., Hill, F.: Multimodal few-shot learning with frozen language models. arXiv preprint arXiv:2106.13884 (2021)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
Wu, S., He, Y.: Enriching pre-trained language model with entity information for relation classification. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2361–2364 (2019)
Xiong, C., Power, R., Callan, J.: Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1271–1279. WWW 2017, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052558
Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 35–45. Association for Computational Linguistics, Copenhagen, Denmark (2017). https://doi.org/10.18653/v1/D17-1004, https://aclanthology.org/D17-1004
Zhong, Z., Friedman, D., Chen, D.: Factual probing is [MASK]: learning vs. learning to recall. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5017–5033. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.naacl-main.398, https://aclanthology.org/2021.naacl-main.398
Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 207–212 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, J. et al. (2022). An Exploration of Prompt-Based Zero-Shot Relation Extraction Method. In: Sun, M., et al. Chinese Computational Linguistics. CCL 2022. Lecture Notes in Computer Science(), vol 13603. Springer, Cham. https://doi.org/10.1007/978-3-031-18315-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-18315-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18314-0
Online ISBN: 978-3-031-18315-7
eBook Packages: Computer ScienceComputer Science (R0)