Improving Generalization by Incorporating Coverage in Natural Language Inference
Table 3: Impact of using coverage for improving generalization across the datasets of similar tasks. Both models are trained on the SQuAD training data.
['[EMPTY]', 'in-domain SQuAD', 'in-domain SQuAD', 'out-of-domain QA-SRL', 'out-of-domain QA-SRL']
Table 3 shows the impact of coverage for improving generalization across these two datasets that belong to the two similar tasks of reading comprehension and QA-SRL. [CONTINUE] The models are evaluated using Exact Match (EM) and F1 measures, [CONTINUE] As the results show, incorporating coverage improves the model's performance in the in-domain evaluation as well as the out-of-domain evaluation in QASRL.