Lessons from Natural Language Inference in the Clinical Domain
Table 4: Baseline accuracy on the development and the test set of MedNLI for different models.
['[BOLD] Set', '[BOLD] Features', '[BOLD] BOW', '[BOLD] InferSent', '[BOLD] ESIM']
Absolute gain in accuracy with respect to the baseline Bold indicates the best source domain for each model and transfer. The feature-based system performed the worst. As for neural networks-based systems, the BOW model showed the lowest performance on the both development and test sets. The InferSent model, in contrast, achieved the highest accuracy, despite ESIM outperforming it on SNLI. This could be attributed to the fact that ESIM has twice as many parameters as InferSent, and so InferSent overfits less to the smaller MedNLI dataset.