Year: 2017
Type: conference-paper
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 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 Dist-Mult model.Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyperparameter tuning or different training objectives.This should prompt future research to re-consider how the performance of models is evaluated and reported.
DOI: 10.18653/v1/w17-2609
Authors: Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst
Institutions: IBM (United States), IBM (Czechia)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2619583282