Argument Role Labelling Question-Answer Pair Data Augmentation
One sentence summary: Augment question-answer pairs with semantic and syntactic constraints to solve the argument role labeling task.
[What] This work explores the QA augmentation for events in news articles. Our altimate goal is to automatically extract structured events from unlabeled unstructured articles. In this work, we focus on a subtask: extract QA pairs for the arguments given an event. The synthetic data then severs as augmented data to train the QA event extraction model.
|Arument Role Label||Question||Answer|
|Who is married?||They|
|Where is someone married?||Spain|
[Why] Traditionally, to extract structured event QA pairs from an article, the steps are: ask questions → extract answers. However, to augment event QA pairs, we propose a reversed approach: extract answers → ask questions. This approach takes advantage of the transfer ability of QA datasets and circumvents the high proportion (~60%) of no-answer questions.See our paper and code.