Exploring Task Difficulty for Few-Shot Relation Extraction
Jiale Han, Bo Hao Cheng, Wei Lu · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances.Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations.Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRE tasks, where the relations are fine-grained and similar to each other.We argue this is largely because existing models do not distinguish hard tasks from easy ones in the learning process.In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information.We further design a method that allows the model to adaptively learn how to focus on hard tasks.Experiments on two standard datasets demonstrate the effectiveness of our method.