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Papers/MLMLM: Link Prediction with Mean Likelihood Masked Languag...

MLMLM: Link Prediction with Mean Likelihood Masked Language Model

Louis Clouatre, Philippe Trempe, Amal Zouaq, Sarath Chandar

2020-09-15Findings (ACL) 2021 8PredictionLanguage ModellingLink Prediction
PaperPDF

Abstract

Knowledge Bases (KBs) are easy to query, verifiable, and interpretable. They however scale with man-hours and high-quality data. Masked Language Models (MLMs), such as BERT, scale with computing power as well as unstructured raw text data. The knowledge contained within those models is however not directly interpretable. We propose to perform link prediction with MLMs to address both the KBs scalability issues and the MLMs interpretability issues. To do that we introduce MLMLM, Mean Likelihood Masked Language Model, an approach comparing the mean likelihood of generating the different entities to perform link prediction in a tractable manner. We obtain State of the Art (SotA) results on the WN18RR dataset and the best non-entity-embedding based results on the FB15k-237 dataset. We also obtain convincing results on link prediction on previously unseen entities, making MLMLM a suitable approach to introducing new entities to a KB.

Results

TaskDatasetMetricValueModel
Link PredictionWN18RRHits@10.4391MLMLM
Link PredictionWN18RRHits@100.611MLMLM
Link PredictionWN18RRHits@30.5418MLMLM
Link PredictionWN18RRMR1603MLMLM
Link PredictionWN18RRMRR0.5017MLMLM
Link PredictionFB15k-237Hits@10.1871MLMLM
Link PredictionFB15k-237Hits@100.4026MLMLM
Link PredictionFB15k-237Hits@30.282MLMLM
Link PredictionFB15k-237MR411MLMLM
Link PredictionFB15k-237MRR0.2591MLMLM

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