Knowledge Base Completion: Baselines Strike Back
This is an incremental critique for researchers in knowledge base completion, highlighting issues in evaluation practices.
The paper tackles the problem of evaluating knowledge base completion models by showing that a tuned baseline (DistMult) outperforms most recent models on the FB15k dataset, casting doubt on claimed architectural improvements.
Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline - our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported.