Towards Enabling Feedback on Rhetorical Structure with Neural Sequence Models

Abstract

Analysis of student writing, both for assessment and for enabling feedback have been of interest to the field of learning analytics. While much progress can be made through detection of local cues in writing, structured prediction approaches offer capabilities that are particularly well tailored to the needs of models aiming to offer substantive feedback on rhetorical structure. We thus cast the analysis of rhetorical structure in academic writing as a structured prediction task in which we employ models that leverage both local and global cues in writing. In particular, this paper presents a hierarchical neural architecture that performs this task. The evaluation demonstrates that the architecture achieves near-human performance while significantly surpassing state-of-the-art baselines. A multifaceted approach to model interpretation offers insights into the inner workings of the model.