Relation Module for Non-answerable Prediction on Reading Comprehension
Table 1: Model performance on SQuAD 2.0 development set averaged over three random seeds.
['[BOLD] Model', '[BOLD] EM(%)', '[BOLD] F1(%)']
Our proposed relation module improves the overall F1 and EM accuracy: 2.0% gain on EM and 1.8% gain on F1 on the BiDAF, as well as 0.8% gain on EM and 1.0% gain on F1 on the BERT-base model. Our relation module is able to take relational information between object-pairs and form a better no-answer prediction than a model without it. The module obtains less gain (0.5% gain of F1) on BERT large model due to the better performance of BERT large model. This module is reader independent and works for any reading comprehension model related to non-answerable tasks.