Professional Text Review Under Limited Sampling Constraints

Text review is a task that determines whether the knowledge expression in a student answer is consistent with a given reference answer. In the professional scenarios, the number of labeled samples is limited, usually ranging from dozens to hundreds, which makes the text review task more challenging. This paper proposes a text review method based on data augmentation, which is performed by the combination of different positive and negative labeled samples. The review model infers the unlabeled samples, where the pseudo-labeled samples with the high confidences are selected for the subsequent training rounds. Experimental results in real national qualification exam datasets show that our method has improvement compared with the traditional method on the text review task under the limited sampling constraints.