Bidirectional matching and aggregation network for few-shot relation extraction - Wei, Zhongcheng; Guo, Wenjie; Zhang, Yunping; Zhang, Jieying; Zhao, Jijun (PeerJ Computer Science, 2023)
Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN.
Keywords: Relation extraction; Few-shot learning; Prototypical network; Knowledge graph; Long-tail distribution