Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering

TL;DR

This paper introduces a representation mapping method for relation detection in knowledge base question answering, significantly improving unseen relation detection while maintaining performance on seen relations.

Contribution

It proposes a simple, adversarial and reconstruction-based mapping approach to handle unseen relations in relation detection tasks.

Findings

Improved accuracy on unseen relations.

Maintained competitive performance on seen relations.

Reorganized dataset to evaluate unseen relation detection.

Abstract

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.