Papers by Ben Dodge

Copied to clipboard

Challenge:	Prior work has focused on training one network on multiple datasets to build a model that performs well on all of the training datasets and generalizes and transfers better to new datasets.
Approach:	They combine multiple reading comprehension datasets to build a multi-dataset question answering model with an ensemble of single-data set experts.
Outcome:	The proposed model outperforms baseline models in in-distribution accuracy and generalization and transfer performance.