Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction

Understand

We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction.

• Our approach allows for more flexible manipulation of text and thus can generate more diverse data while keeping the original event structure unchanged as much as possible.
• Specifically, it first randomly masks out an adjunct sentence fragment and then infills a variable-length text span with a fine-tuned infilling model.
• The main advantage lies in that it can replace a fragment of arbitrary length in the text with another fragment of variable length, compared to the existing methods which can only replace a single word or a fixed-length fragment.