DSReg: Using Distant Supervision as a Regularizer
Table 5: Results on the NarrativeQA dataset.
['Model', 'Standard  [ITALIC] L1', 'DSReg ( [ITALIC] L1+ [ITALIC] L2)', 'DSReg ( [ITALIC] L1+ [ITALIC] L2+ [ITALIC] L3)', 'Human']
We followed the criteria in Kočiskỳ et al. We used the splits of positive examples, hard-negative examples and easy-negative examples to train DSReg models, using both the three-class classification and the (positive+hard-neg) vs. easy-neg as regulations. When combined with BiDAF, the proposed DSReg model outperforms the standard BiDAF model in both settings. This sets new state-of-the-art results for the summary setting on the NarrativeQA dataset.