Abstract
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 like 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.- Anthology ID:
- W17-2609
- Volume:
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 69–74
- Language:
- URL:
- https://aclanthology.org/W17-2609
- DOI:
- 10.18653/v1/W17-2609
- Cite (ACL):
- Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. 2017. Knowledge Base Completion: Baselines Strike Back. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 69–74, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Knowledge Base Completion: Baselines Strike Back (Kadlec et al., RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/W17-2609.pdf