  Essays as a form of assessment test student knowledge on a deeper level than
short answer and multiple-choice questions. However, the manual evaluation of
essays is time- and labor-consuming. Automatic clustering of essays, or their
fragments, prior to manual evaluation presents a possible solution to reducing
the effort required in the evaluation process. Such clustering presents
numerous challenges due to the variability and ambiguity of natural language.
In this paper, we introduce two datasets of undergraduate student essays in
Finnish, manually annotated for salient arguments on the sentence level. Using
these datasets, we evaluate several deep-learning embedding methods for their
suitability to sentence clustering in support of essay grading. We find that
the choice of the most suitable method depends on the nature of the exam
question and the answers, with deep-learning methods being capable of, but not
guaranteeing better performance over simpler methods based on lexical overlap.
