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This paper aims to enhance the few-shot relation classification especiallyfor sentences that jointly describe multiple relations. Due to the fact thatsome relations usually keep high co-occurrence in the same context, previousfew-shot relation classifiers struggle to distinguish them with few annotatedinstances. To alleviate the above relation confusion problem, we propose CTEG,a model equipped with two mechanisms to learn to decouple these easily-confusedrelations. On the one hand, an Entity-Guided Attention (EGA) mechanism, whichleverages the syntactic relations and relative positions between each word andthe specified entity pair, is introduced to guide the attention to filter outinformation causing confusion. On the other hand, a Confusion-Aware Training(CAT) method is proposed to explicitly learn to distinguish relations byplaying a pushing-away game between classifying a sentence into a true relationand its confusing relation. Extensive experiments are conducted on the FewReldataset, and the results show that our proposed model achieves comparable andeven much better results to strong baselines in terms of accuracy. Furthermore,the ablation test and case study verify the effectiveness of our proposed EGAand CAT, especially in addressing the relation confusion problem.

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training