Uncertainty-Aware Self-Training for Semi-Supervised Event Temporal Relation Extraction

Abstract

Extracting event temporal relations is an important task for natural language understanding. Many works have been proposed for supervised event temporal relation extraction, which typically requires a large amount of human-annotated data for model training. However, the data annotation for this task is very time-consuming and challenging. To this end, we study the problem of semi-supervised event temporal relation extraction. Self-training as a widely used semi-supervised learning method can be utilized for this problem. However, it suffers from the noisy pseudo-labeling problem. In this paper, we propose the use of uncertainty-aware self-training framework (UAST) to quantify the model uncertainty for coping with pseudo-labeling errors. Specifically, UAST utilizes (1) Uncertainty Estimation module to compute the model uncertainty for pseudo-labeling unlabeled data; (2) Sample Selection with Exploration module to select informative samples based on uncertainty estimates; and (3) Uncertainty-Aware Learning module to explicitly incorporate the model uncertainty into the self-training process. Experimental results indicate that our approach significantly outperforms previous state-of-the-art methods.