Negated and Misprimed Probes for Pretrained Language Models:Birds Can Talk, But Cannot Fly
Table 5: Accuracy of BERT on balanced corpus. Pretrained BERT does not model negation well, but finetuned BERT classifies sentences as true/false correctly.
['[EMPTY]', '[BOLD] train pos', '[BOLD] train neg', '[BOLD] test pos', '[BOLD] test neg']
The learning curves (see appendix) show that this is not due to overfitting the training data. While the training loss rises, the test precision fluctuates around a plateau. However, if we finetune BERT (“finetuned BERT”) on the task of classifying sentences as true/false, its test accuracy is 100%. (Recall that false sentences simply correspond to true sentence with a “not” inserted or removed.) So BERT easily learns negation if supervision is available, but fails without it. This experiment demonstrates the difficulty of learning negation through unsupervised pretraining. We suggest that the inability of pretrained BERT to distinguish true from false is a serious impediment to accurately handling factual knowledge. We suspect that a possible explanation for PLMs’ poor performance is that negated sentences occur much less frequently in training corpora. However, the PLM objective encourages the model to predict fillers based on similar sentences in the training corpus – and if the most similar statement to a negative sentence is positive, then the filler is generally incorrect. However, after finetuning, BERT is able to classify truth/falseness correctly, demonstrating that negation can be learned through supervised training.