  Relation detection is a core step in many natural language process
applications including knowledge base question answering. Previous efforts show
that single-fact questions could be answered with high accuracy. However, one
critical problem is that current approaches only get high accuracy for
questions whose relations have been seen in the training data. But for unseen
relations, the performance will drop rapidly. The main reason for this problem
is that the representations for unseen relations are missing. In this paper, we
propose a simple mapping method, named representation adapter, to learn the
representation mapping for both seen and unseen relations based on previously
learned relation embedding. We employ the adversarial objective and the
reconstruction objective to improve the mapping performance. We re-organize the
popular SimpleQuestion dataset to reveal and evaluate the problem of detecting
unseen relations. Experiments show that our method can greatly improve the
performance of unseen relations while the performance for those seen part is
kept comparable to the state-of-the-art. Our code and data are available at
https://github.com/wudapeng268/KBQA-Adapter.
