Abstract
The task of few-shot relation extraction presents a significant challenge as it requires predicting the potential relationship between two entities based on textual data using only a limited number of labeled examples for training. Recently, quite a few studies have proposed to handle this task with task-agnostic and task-specific weights, among which prototype networks have proven to achieve the best performance. However, these methods often suffer from overfitting novel relations because every task is treated equally. In this paper, we propose a novel methodology for prototype representation learning in task-adaptive scenarios, which builds on two interactive features: 1) common features are used to rectify the biased representation and obtain the relative class-centered prototype as much as possible, and 2) discriminative features help the model better distinguish similar relations by the representation learning of the entity pairs and instances. We obtain the hybrid prototype representation by combining common and discriminative features to enhance the adaptability and recognizability of few-shot relation extraction. Experimental results on FewRel dataset, under various few-shot settings, showcase the improved accuracy and generalization capabilities of our model.
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
References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Brody, S., Wu, S., Benton, A.: Towards realistic few-shot relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 5338–5345 (2021)
Chen, M., Zhang, W., Zhang, W., Chen, Q., Chen, H.: Meta relational learning for few-shot link prediction in knowledge graphs. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4217–4226 (2019)
Cheng, P., et al.: Improving disentangled text representation learning with information-theoretic guidance. arXiv preprint: arXiv:2006.00693 (2020)
Dong, B., et al.: Meta-information guided meta-learning for few-shot relation classification. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1594–1605 (2020)
Dong, M., Pan, C., Luo, Z.: MapRE: an effective semantic mapping approach for low-resource relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2694–2704 (2021)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)
Gao, T., Han, X., Liu, Z., Sun, M.: Hybrid attention-based prototypical networks for noisy few-shot relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6407–6414 (2019)
Gao, T., et al.: FewRel 2.0: towards more challenging few-shot relation classification. arXiv preprint: arXiv:1910.07124 (2019)
Garcia, V., Bruna, J.: Few-shot learning with graph neural networks. arXiv preprint: arXiv:1711.04043 (2017)
Han, J., Cheng, B., Lu, W.: Exploring task difficulty for few-shot relation extraction. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2605–2616 (2021)
Han, M., et al.: Not all instances contribute equally: Instance-adaptive class representation learning for few-shot visual recognition. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Han, X., et al.: FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. arXiv preprint: arXiv:1810.10147 (2018)
Lai, N., Kan, M., Han, C., Song, X., Shan, S.: Learning to learn adaptive classifier-predictor for few-shot learning. IEEE Trans. Neural Netw. Learn. Syst. 32(8), 3458–3470 (2020)
Lee, W.Y., Wang, J.Y., Wang, Y.C.F.: Domain-agnostic meta-learning for cross-domain few-shot classification. In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1715–1719. IEEE (2022)
Li, W.H., Liu, X., Bilen, H.: Cross-domain few-shot learning with task-specific adapters. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7161–7170 (2022)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Liu, Y., Hu, J., Wan, X., Chang, T.H.: Learn from relation information: towards prototype representation rectification for few-shot relation extraction. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 1822–1831 (2022)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint: arXiv:1711.05101 (2017)
Nasution, M.K.: Social network mining (SNM): a definition of relation between the resources and SNA. arXiv preprint: arXiv:2207.06234 (2022)
Peng, H., et al.: Learning from context or names? An empirical study on neural relation extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3661–3672 (2020)
Popovic, N., Färber, M.: Few-shot document-level relation extraction. arXiv preprint: arXiv:2205.02048 (2022)
Qu, M., Gao, T., Xhonneux, L.P., Tang, J.: Few-shot relation extraction via Bayesian meta-learning on relation graphs. In: International Conference on Machine Learning, pp. 7867–7876. PMLR (2020)
Quan, C., Wang, M., Ren, F.: An unsupervised text mining method for relation extraction from biomedical literature. PLoS ONE 9(7), e102039 (2014)
Ren, H., Cai, Y., Chen, X., Wang, G., Li, Q.: A two-phase prototypical network model for incremental few-shot relation classification. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1618–1629 (2020)
Ren, H., Cai, Y., Lau, R.Y.K., Leung, H.F., Li, Q.: Granularity-aware area prototypical network with bimargin loss for few shot relation classification. IEEE Trans. Knowl. Data Eng. 35(5), 4852–4866 (2022)
Simon, C., Koniusz, P., Nock, R., Harandi, M.: Adaptive subspaces for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4136–4145 (2020)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Tran, V.H., Ouchi, H., Watanabe, T., Matsumoto, Y.: Improving discriminative learning for zero-shot relation extraction. In: Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge, pp. 1–6. Association for Computational Linguistics, Dublin, Ireland and Online (2022). https://doi.org/10.18653/v1/2022.spanlp-1.1, https://aclanthology.org/2022.spanlp-1.1
Wang, M., Zheng, J., Cai, F., Shao, T., Chen, H.: DRK: discriminative rule-based knowledge for relieving prediction confusions in few-shot relation extraction. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 2129–2140 (2022)
Wang, Y., Salamon, J., Bryan, N.J., Bello, J.P.: Few-shot sound event detection. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 81–85. IEEE (2020)
Xiao, Y., Jin, Y., Hao, K.: Adaptive prototypical networks with label words and joint representation learning for few-shot relation classification. IEEE Trans. Neural Netw. Learn. Syst. (2021)
Yang, K., Zheng, N., Dai, X., He, L., Huang, S., Chen, J.: Enhance prototypical network with text descriptions for few-shot relation classification. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2273–2276 (2020)
Yang, S., Zhang, Y., Niu, G., Zhao, Q., Pu, S.: Entity concept-enhanced few-shot relation extraction. arXiv preprint: arXiv:2106.02401 (2021)
Ye, Z.X., Ling, Z.H.: Multi-level matching and aggregation network for few-shot relation classification. arXiv preprint: arXiv:1906.06678 (2019)
Acknowledgements
The authors would like to thank the Associate Editor and anonymous reviewers for their valuable comments and suggestions. This work is funded in part by the National Natural Science Foundation of China under Grants No.62176029. This work also is supported in part by the Chongqing Technology Innovation and Application Development Special under Grants CSTB2022TIAD-KPX0206. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, W., Zhong, J., Xia, Y., Zhou, Y., Li, R. (2023). Adaptive Prototype Network with Common and Discriminative Representation Learning for Few-Shot Relation Extraction. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-46674-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46673-1
Online ISBN: 978-3-031-46674-8
eBook Packages: Computer ScienceComputer Science (R0)