Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring
This addresses the challenge of noisy student essays for educators and researchers, offering a parser-free method that is incremental in improving discourse-aware text representation.
The paper tackles the problem of automated essay scoring by proposing an unsupervised pre-training approach that captures discourse structure without needing parsers or annotations, achieving new state-of-the-art results on the essay Organization scoring task.
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this paper, we propose an unsupervised pre-training approach to capture discourse structure of essays in terms of coherence and cohesion that does not require any discourse parser or annotation. We introduce several types of token, sentence and paragraph-level corruption techniques for our proposed pre-training approach and augment masked language modeling pre-training with our pre-training method to leverage both contextualized and discourse information. Our proposed unsupervised approach achieves new state-of-the-art result on essay Organization scoring task.