Dynamic Sampling Strategies for Multi-Task Reading Comprehension (2020.acl-main)
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| Challenge: | Prior work focused on model architecture or generalization to held out datasets and largely passed over the particulars of the multi-task learning set up. |
| Approach: | They propose a dynamic sampling strategy that selects instances proportional to the model's current performance on a dataset relative to its single task performance. |
| Outcome: | The proposed model outperforms the best model on ORB, a recent multitask reading comprehension benchmark. |
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