Papers with augmentation policies
Automatically Learning Data Augmentation Policies for Dialogue Tasks (D19-1)
Copied to clipboard
| Challenge: | Recent advances in automatic data augmentation have focused on computer vision tasks where it is easy to apply imperceptible perturbations without changing an image’s semantic meaning. |
| Approach: | They adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation. |
| Outcome: | The proposed algorithm reduces data-level model bias by using a controller trained on the target task. |