  Although BERT based relation classification (RC) models have achieved
significant improvements over the traditional deep learning models, it seems
that no consensus can be reached on what is the optimal architecture. Firstly,
there are multiple alternatives for entity span identification. Second, there
are a collection of pooling operations to aggregate the representations of
entities and contexts into fixed length vectors. Third, it is difficult to
manually decide which feature vectors, including their interactions, are
beneficial for classifying the relation types. In this work, we design a
comprehensive search space for BERT based RC models and employ neural
architecture search (NAS) method to automatically discover the design choices
mentioned above. Experiments on seven benchmark RC tasks show that our method
is efficient and effective in finding better architectures than the baseline
BERT based RC model. Ablation study demonstrates the necessity of our search
space design and the effectiveness of our search method.
