ERNIE: Enhanced Language Representation with Informative Entities
Table 2: Results of various models on FIGER (%).
['Model', 'Acc.', 'Macro', 'Micro']
From the results, we observe that: (1) BERT achieves comparable results with NFGEC on the macro and micro metrics. However, BERT has lower accuracy than the best NFGEC model. As strict accuracy is the ratio of instances whose predictions are identical to human annotations, it illustrates some wrong labels from distant supervision are learned by BERT due to its powerful fitting ability. (2) Compared with BERT, ERNIE significantly improves the strict accuracy, indicating the external knowledge regularizes ERNIE to avoid fitting the noisy labels and accordingly benefits entity typing.