  Continual relation extraction is an important task that focuses on extracting
new facts incrementally from unstructured text. Given the sequential arrival
order of the relations, this task is prone to two serious challenges, namely
catastrophic forgetting and order-sensitivity. We propose a novel
curriculum-meta learning method to tackle the above two challenges in continual
relation extraction. We combine meta learning and curriculum learning to
quickly adapt model parameters to a new task and to reduce interference of
previously seen tasks on the current task. We design a novel relation
representation learning method through the distribution of domain and range
types of relations. Such representations are utilized to quantify the
difficulty of tasks for the construction of curricula. Moreover, we also
present novel difficulty-based metrics to quantitatively measure the extent of
order-sensitivity of a given model, suggesting new ways to evaluate model
robustness. Our comprehensive experiments on three benchmark datasets show that
our proposed method outperforms the state-of-the-art techniques. The code is
available at the anonymous GitHub repository:
https://github.com/wutong8023/AAAI_CML.
