What’s in a Name? Are BERT Named Entity Representations just as Good for any other Name?
Table 5: Adversarial Evaluation of BERT on different tasks comparing the accuracy on the original model against our algorithm with a canonical dictionary of size (M) 1 or 3. For Random Replacement dataset, mean across the ten artificial datasets along with standard deviation in brackets is presented
['Dataset', 'NLI(ORG) F1 Original', 'NLI(ORG) F1 RESEMBLE', 'NLI(ORG) F1 RESEMBLE', 'GEC(PER) F0.5 Original', 'GEC(PER) F0.5 RESEMBLE', 'GEC(PER) F0.5 RESEMBLE', 'CoRef(PER) F1 Original', 'CoRef(PER) F1 RESEMBLE', 'CoRef(PER) F1 RESEMBLE']
We perform defense on four datasets – Original, Best, Worst, Random Replacement. For random replacement, we constructed 10 new datasets from the original dataset with its names replaced with randomly selected names, and then evaluate the performance of our models on these datasets. We present the mean and standard deviation of the F scores across these newly constructed datasets. For best and worst we evaluate performance on datasets generated from Algo. First observe that accuracy of even the original test dataset improves with our simple replacement ensembling while reducing the variance. For example, for GEC F score increases from 50.93 to 51.81. The variance has also reduced as seen for the random replacement datasets. The adversarial accuracy improves significantly — for CoRef we see a jump of DWorst from 60.91 to 68.31 and for GEC the gains are even higher. The difference between the best and worst accuracy reduces drastically. Although for DBest accuracy drops with RESEMBLE, the overall gains across the three dataset variants are much higher. Further a single canonical entry M=1 is almost as effective as larger ensembles of M=3. This implies that at test-time, we have to deploy the model on at most two instances to enjoy significantly higher robustness. This shows that replacement with canonical entities while accounting for uncertainty of entity identification is a viable alternative to enhance robustness.