Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction (2023)

Abstract: Distantly supervised relation extraction (DSRE) aims to extract relational facts from texts but suffers from noisy instances. To mitigate the influence of noisy labels, current methods typically use the Multi-Instance-Learning framework to extract relations for each bag. However, these approaches are not capable of extracting relation labels for individual sentences. Several studies have focused on sentence-level DSRE to solve the above problem. These studies primarily aim to develop methods for identifying noisy samples and filtering them out to mitigate the impact of noise. However, discarding noisy samples directly leads to the loss of useful information. To this end, we propose SSLRE, a novel Semi-Supervised-Learning Relation Extraction framework for sentence-level DSRE. We discard only the labels of the noisy samples and utilize these instances without labels as unlabeled samples. Our SSLRE framework utilizes a weighted K-NN graph to select confident samples as labeled data and the rest as unlabeled. We then design a robust semi-supervised learning framework that can efficiently handle remaining label noise present in the labeled dataset, while also making effective use of unlabeled samples. Based on our experiments on two real-world datasets, the SSLRE framework we proposed has achieved significant enhancements in sentence-level relation extraction performance compared to the existing state-of-the-art methods. Moreover, it has also attained a state-of-the-art level of performance in bag-level relation extraction with ONE aggregation strategy.

## introduction relation extraction (re) is a fundamental process for constructing knowledge graphs, as it aims to predict the relationship between entities based on their context. however, most supervised re states. obama is the 44th president of the united states. obama is a household name in the united states. techniques require extensive labeled training data, which can be difficult to obtain manually. to address this issue, distant supervision (ds) was proposed @xcite to automatically generate labeled text corpus by aligning plain texts with knowledge bases (kb). for instance, if a sentence contains both the subject (s) and object (o) of a triple ⟨s, r, o⟩ (⟨subject, relation, object⟩), then the ds method considers ⟨s, r, o⟩ as a valid sample for that sentence. conversely, if no relational triples apply, then the sentence is labeled as "na". distantly supervised datasets usually face high label noise in training data due to the annotation process. to mitigate the impact of noisy labels caused by distant supervision, contemporary techniques @xcite @xcite @xcite usually employ multi-instance-learning (mil) framework or modify mil to train the relation extraction model. although mil-based techniques can identify bag relation labels, they are not proficient in precisely mapping each sentence in a bag with explicit sentence labels @xcite @xcite . several studies have focused on improving sentence-level dsre and have empirically demonstrated the inadequacy of bag-level methods on sentence-level evaluation. @xcite apply reinforcement learning to train a sample selector. @xcite iden-tify confident samples by frequent patterns. @xcite utilizes negative training to separate noisy data from the training data. however, these methods has two main issues: (1) these works simply discard all noisy samples and train a relation extraction model with selected samples. however, filtering out noisy samples directly results in the loss of useful information. @xcite notes that the dsre dataset has a noiserate exceeding 50%. discarding all these samples would result in a significant loss of information. to address the two issues, this work proposes a novel semi-supervised-learning framework for sentence-level dsre, first, we construct a k-nn graph for all samples using the hidden features. then, we identify confident samples from the graph structure and consider the remaining samples as noisy. for issue (1): our method discards only the noisy labels and treats corresponding samples as unlabeled data. we then utilize this unlabeled data by pseudo labeling within our robust semi-supervised learning framework to learn a better feature representation for relation. for issue (2): despite our initial selection of confident samples, there may still be noise in the labeled dataset. we have developed a noise-robust semi-supervised learning framework that leverages mixup supervised contrastive learning to learn from the labeled dataset and curriculum pseudo labeling to learn from the unlabeled dataset. to summarize the contribution of this work: ## methodology to achieve sentence-level relation extraction in dsre, we propose a framework called sslre, which consists of two main steps. firstly, we select confident samples from the distantly supervised dataset using a weighted k-nn approach built by all sample representations. we use the selected samples as labeled data and the remaining samples as unlabeled data (as detailed in section 3.1). secondly, we employ our robust semi-supervised learning framework to learn from the semi-supervised datasets (as described in section 3.2). appendix a delineates the full algorithm. ## conclusion in this paper, we propose sslre, a novel sentence-level framework that is grounded in semi-supervised learning for the dsre task. our sslre framework employs mixup supervised contrastive learning to tackle the noise present in selected samples, and it leverages unlabeled samples through pseudo labeling, which effectively utilize the information contained within noisy samples. experimental results demonstrate that sslre outperforms strong baseline methods in both sentencelevel and bag-level relation extraction.