Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction (2022)

Abstract: Supervised open relation extraction aims to discover novel relations by leveraging supervised data of pre-defined relations. However, most existing methods do not achieve effective knowledge transfer from pre-defined relations to novel relations, they have difficulties generating high-quality pseudo-labels for unsupervised data of novel relations and usually suffer from the error propagation issue. In this paper, we propose a Cluster-aware Pseudo-Labeling (CaPL) method to improve the pseudo-labels quality and transfer more knowledge for discovering novel relations. Specifically, the model is firstly pre-trained with the pre-defined relations to learn the relation representations. To improve the pseudo-labels quality, the distances between each instance and all cluster centers are used to generate the cluster-aware soft pseudo-labels for novel relations. To mitigate the catastrophic forgetting issue, we design the consistency regularization loss to make better use of the pseudo-labels and jointly train the model with both unsupervised and supervised data. Experimental results on two public datasets demonstrate that our proposed method achieves new state-of-the-arts performance.

## introduction open relation extraction (openre) has been proposed to extract the novel relations that are not defined or observed beforehand. previous methods can be classified into two types: unsupervised and supervised. unsupervised openre @xcite @xcite @xcite regards the openre as a totally unsupervised task which first extracts the feature and then clusters them. however, these methods don't take full advantage of the large amounts of existing relational hard pseudo label 1 s 2 s 3 s soft pseudo label 1 s 2 s 3 s henri was born in paris : 2 s tom was born in 2008 : 3 s : 1 s tom was born in chi cago figure 1 : relation instance s 1 and s 3 belong to the same relation type while s 2 with similar context is from another relation type. however, the hard pseudo-labels produced by common clustering methods contain much noise which causes the error propagation issue. we generate soft pseudo-labels that contain the information about true cluster to improve the pseudo-labels quality. since the classes between pre-defined relations and novel relations are disjoint, the main challenge of supervised openre is how to make the best use of the prior knowledge in pre-defined relations to extract novel relations. @xcite proposes relational siamese networks to transfer the knowledge from pre-defined relations to novel relations. however, many studies have shown that high-dimensional embeddings learn much about the complex linguistic information @xcite @xcite , which makes it hard to produce ideal clusters. @xcite proposes a relationoriented clustering method that explicitly aligns the derived clusters with relational semantic classes. however, we find that the pseudo-labels produced by previous method are not robust to transfer the knowledge from pre-defined relations to novel relations which means that using unreliable pseudolabels could cause the error propagation issue. as is shown in figure 1 , relation instance s 1 and s 3 belong to the same relation type born_in_place while s 2 with similar context is from another relation type born_in_date. owing to the spurious correlations @xcite , existing method that selects the nearest cluster center as the pseudolabels may produce noise pseudo-labels. hence if we further exploit the hard pseudo-labels, the model would be wrongly optimized. we argue that utilizing the information about all cluster centers to generate the soft pseudo-labels would be helpful to reduce the error propagation issue. the intuition is that if we exploit the soft pseudo-labels, we can utilize the information about the true cluster to guide the learning for discovering novel relations. in this paper, we propose a cluster-aware pseudo-labeling (capl) method to improve the pseudo-labels quality and transfer more knowledge for discovering novel relations. firstly, we pre-train the model under the supervision of cross-entropy loss to leverage the prior knowledge in pre-defined relations. then, to effectively transfer the knowledge, rather than directly using the hard pseudolabels produced by common clustering algorithms, we use the distances between each instance and all cluster centers to generate cluster-aware soft pseudo-labels for novel relations. finally, we design consistency regularization loss to make the best use of the knowledge stored in the clusteraware pseudo-labels and jointly train the model with both supervised and unsupervised data to mitigate the catastrophic forgetting issue. to summarize, the major contributions of our work are as follows: (1) we propose a simple but effective framework based on the capl for supervised openre whicn can transfer more knowledge for discovering novel relations. (2) we design the consistency regularization loss to keep the cluster predictions and pseudo-labels of unsupervised data to be consistent for making better use of the pseudolabels. (3) experimental results and analyses on two public datasets demonstrate the effectiveness of our proposed method. ## method in the supervised openre settings, training data is split into two sets: a supervised dataset of predefined relations d @xmath0, y l i , @xmath1, . . . , n and an unsupervised dataset of novel relations in d u is an relation instance and y l i is a categorical label. our goal is to cluster the d u to discover c u novel relations where we assume c u to be known a priori. the set of c l labeled classes is assumed to be disjoint from the set of c u unlabeled classes. in this work, we propose a simple but effective framework based on the capl to improve the pseudo-labels quality for discovering novel relations. figure 2 shows the overall architecture of our proposed method. we will introduce these step details in the following subsections. ## conclusion in this paper, we propose an effective framework based on cluster-aware pseudo-labeling (capl) to transfer more knowledge for discovering novel relations. our main contribution is to improve the knowledge transfer ability of the model. the proposed method makes better use of the prior knowledge in pre-defined relations and generalizes to novel relations with the high-quality pseudo-labels. experiments and analyses confirm the effectiveness of capl for supervised openre.