KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Table 6: Accuracies (%) on the FewRel dataset. N-K indicates the N-way K-shot setting. “Proto” indicates Prototypical Networks (snell2017prototypical), “PAIR” is from gao-etal-2019-fewrel and “MTB” is from baldini-soares-etal-2019-matching. MTB uses the LARGE size and all the other models use the BASE size.
['Model', 'FewRel 1.0 5-1', 'FewRel 1.0 5-5', 'FewRel 1.0 10-1', 'FewRel 1.0 10-5', 'FewRel 2.0 5-1', 'FewRel 2.0 5-5', 'FewRel 2.0 10-1', 'FewRel 2.0 10-5']
We also compare our model with MTB (baldini-soares-etal-2019-matching). Note that MTB (1) uses the LARGE architecture of BERT and (2) has a test set leaking problem since it performs pre-training over the whole Wikipedia and Wikidata, which are the source of FewRel.