Semi-supervised Relation Extraction via Incremental Meta Self-Training (2021)

Abstract: To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates accurate quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.

## introduction relation extraction plays a key role in transforming massive corpus into structured triplets (subject, relation, object). for example, given a sentence: the song was composed for a famous brazilian musician, we can extract a relation product-producer between two entities song and musician. these triples can be used in various downstream applications such as web search, sentiment analysis and question answering. current relation extraction methods @xcite can discover the semantic relation that holds between two entities under supervised learning. however, these methods typically require lots of manually labeled data for model training. while in practice, these labeled data would be laborintensive to obtain and error-prone due to human subjective judgments. a lot of work is being explored to alleviate the human supervision in relation extraction. distant supervision methods @xcite leverage external knowledge bases to obtain annotated triplets as the supervision. these methods make a strong assumption that the relation between entity pairs should not depend on the context, which leads to context-agnostic label noises and sparse matching results. semi-supervised relation extraction method aims to leverage large amounts of unlabeled data to augment limited labeled data. there are two major ways to make fully use of unlabeled data, i.e., self-ensembling method and self-training method. self-ensembling methods @xcite assume that predictions on the unlabeled data by the model should remain unchanged, even if there are perturbations in the model parameters or training data. self-ensembling methods usually suffer from insufficient supervision -when labeled data is limited, the model is relation classification network cτ relation label generation network cη labeled data unlabeled data pseudo label generation pseudo label selection pseudo label exploitation pseudo label data .8 .7 .8 .9 .6 .8 .7 .9 .5 .8 .7 .8 .9 .8 .7 .9 update cτ meta update cη updated cτ+ reluctant to acquire new relation knowledge that could be learned from the unlabeled data and thus impede further improvements. on the contrary, self-training methods @xcite improve the predictive ability of the model by obtaining high-confidence labels from unlabeled data incrementally, and retraining the model on the updated labeled data. however, using self-training directly may introduce noisy pseudo labels inevitably, which hurts the model performance, known as the gradual drift problem @xcite . to alleviate the noise in pseudo labels, we design a method that can generate high-quality pseudo labels from the unlabeled dataset as additional supervision. we propose a semi-supervised learning framework which adopts meta-learning during pseudo label generation and automatically learns to reduce the influence of noisy pseudo labels. we also adopt a pseudo label selection and exploitation scheme during each self-training step to take full advantage of high-quality pseudo labels. as shown in figure 1 , the proposed framework has two networks, relation classification network (rcn) and relation label generation network (rlgn). rcn encodes entity pair representations based on the context in which they are mentioned, and train a classifier with both labeled data and pseudo-labeled data generated by rlgn to classify the representations into relation categories. rlgn leverages pseudo label selection/exploitation scheme to calibrate confidence scores and obtain high-quality pseudo labels from unlabeled data for rcn. rlgn is meta optimized by rcn using the labeled data. the main contributions of this work are as follows: ## proposed model the proposed incremental meta self-training relation extraction (metasre) consists of two networks: relation classification network (rcn) and relation label generation network (rlgn). as shown in figure 1 , the input of the rcn is labeled data, which consists of sentences and relation mentions: [sentence, entity 1 , entity 2 , relation]. the goal of the network is to conduct relation classification using both labeled data and unlabeled ones with high-quality pseudo labels generated by the rlgn. in order to alleviate gradual drifts in traditional self-training scheme, the rlgn will be metaoptimized by rcn using the labeled data to insure pseudo label quality. rlgn leverages pseudo label selection scheme to obtain high-confidence pseudo labels with less noises. rcn exploits these pseudo labels in an iterative fashion and further improves its classification performance. ## related work relation extraction focuses on predicting the relation between two entities in a sentence. recent literature leverage deep neural networks to extract features about two entities from sentences, and classify them into specific relations. relation extraction methods are often formulated in a supervised setting @xcite @xcite and require manual annotations on large amounts of corpora, which is labor-intensive to obtain. semi-supervised learning methods have received attention recently, since these methods require fewer labeled data and generate pseudo labels by re-training model and improve the performance iteratively. two major categories of semi-supervised learning methods are related to our problem. one major category is the self-ensembling method @xcite , which is based on the assumption that pseudo label distributions should remain unchanged even if the model parameters and instances have small perturbations. in this case, various models based on consistent data could be used to improve the performance by co-training each other @xcite . however, this method relies heavily on the quality and quantity of labeled data. in our task, the improvement of the model by this method is limited. another category is the self-training method, the work proposed by @xcite incrementally generates pseudo labels from unlabeled data, and uses these pseudo labels to enhance the classification ability of the model. however, this method often needs to endure semantic drift @xcite @xcite , which means the noise generated by pseudo labels in each iteration will be continuously strengthened, causing the model to deviate from the global minimum point. @xcite tries to fine-tune the model on the labeled data in each iteration to avoid noise and construct a prototypical network to exploit pseudo labels. however, the model is still influenced by the noises with the unchanged pseudo labels and spreads these noises to the generated pseudo labels. in our work, we adopt a meta learner network to prevent the model from drifting due to label noise and enables robust iterative self-training. ## conclusion in this paper, we propose a semi-supervised learning model metasre for relation extraction. different from conventional semi-supervised models which directly adopt the classification network to classify unlabeled data into pseudo labels, our model proposes a novel meta-learning-based selftraining network to reduce the noises contained in pseudo labels and avoid the model gradual drift. comparing with using pseudo labels directly, our model leverages pseudo label selection and exploitation scheme to further select high-confidence pseudo labels with low noises. experiments on two popular benchmarks show the effectiveness and consistent improvements over baselines.