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. On trigger and argument extraction tasks, the proposed framework is more effective than baseline methods and it demonstrates particularly strong results in the low-resource setting. Our further analysis shows that it achieves a good balance between diversity and distributional similarity.
翻译:我们为事件提取提供了一个灵活而有效的数据增强框架,我们的方法允许对文字进行更灵活的操作,从而能够产生更多样化的数据,同时尽可能保持原始事件结构不变。具体地说,我们的方法首先随机遮盖附加句的碎片,然后用一个微调的填充模型填充一个可变长的文字,其主要优势在于它能够用另一个变长的碎片取代文本中的任意长度碎片,而现有的方法只能取代一个单词或固定长度的碎片。关于触发和辩论提取任务,拟议框架比基线方法更有效,它显示了低资源环境特别强的结果。我们的进一步分析表明,它能够实现多样性和分布相似性之间的良好平衡。