Model-Agnostic Meta-Learning For Relation Classification With Limited Supervision

In this paper we frame the task of supervised relation classification as an instance of metalearning. We propose a model-agnostic metalearning protocol for training relation classifiers to achieve enhanced predictive performance in limited supervision settings. During training, we aim to not only learn good parameters for classifying relations with sufficient supervision, but also learn model parameters that can be fine-tuned to enhance predictive performance for relations with limited supervision. In experiments conducted on two relation classification datasets, we demonstrate that the proposed meta-learning approach improves the predictive performance of two state-of-the-art supervised relation classification models.