  While automated essay scoring (AES) can reliably grade essays at scale,
automated writing evaluation (AWE) additionally provides formative feedback to
guide essay revision. However, a neural AES typically does not provide useful
feature representations for supporting AWE. This paper presents a method for
linking AWE and neural AES, by extracting Topical Components (TCs) representing
evidence from a source text using the intermediate output of attention layers.
We evaluate performance using a feature-based AES requiring TCs. Results show
that performance is comparable whether using automatically or manually
constructed TCs for 1) representing essays as rubric-based features, 2) grading
essays.
