Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
This work addresses a critical bottleneck in KBQA for handling unseen relations, though it is incremental as it builds on existing embedding methods.
The paper tackles the problem of relation detection in knowledge base question answering, specifically addressing the performance drop for unseen relations by proposing a representation adapter method that improves unseen relation detection while maintaining comparable performance on seen relations.
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.