Instance-Adaptive Attention Mechanism for Relation Classification

Yao Lu, Chunyun Zhang, Weiran Xu

Abstract: Recently, attention mechanism has been transferred to relation classification task. Since relation classification is a sequence-to-label task, the challenge is how to generate the deciding factor to calculate attention weights. The previous solution randomly initializes a global deciding factor, which is easy to suffer from over-fitting. To solve the problem, we propose instance-adaptive attention mechanism, which generates a specially designed deciding factor for each sentence. The experimental result on SemEval-2010 Task 8 dataset shows that our method can outperform most state-of-the-art systems without external linguistic features.