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Significantly decrease ranges of prediction success are obtained for low-frequency masked entity types. We additional explored the statistics that might have been learned from our training sets during finetuning. Based on the annotation of entity mentions, we thought-about a baseline that scores candidate entities by their co-prevalence frequency with the in contrast entity inside the practice set. In response to this context-less baseline, given the query “which one is cuter MASK or cats? “, the highest-ranking reply is canine, as “dogs” are the entity that the majority frequently co-occurs with “cats” within the training examples. Table 6 presents these outcomes, exhibiting that the entity co-prevalence baseline (‘co-happen.’) yields robust outcomes, outperforming the pretrained fashions, and explaining some of the efficiency gains obtained by the finetuned entity-centered RELIC. Still, RELIC outperforms this baseline by a substantial margin. A part of this lies in the linguistic and word knowledge learned by RELIC during the pretraining phase, indicating that deep entity representation generalizes past discrete lexical co-occurrences data.
