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.

本文介绍了两个手动注释的芬兰本科学生论文数据集，旨在评估几种基于深度学习嵌入方法对支持论文评分的句子聚类的适用性。发现选择最合适的方法取决于试题和答案的性质，深度学习方法能够但不能保证在词汇重叠上的简单方法之上表现更好。

论文评分支持中句子聚类的深度学习

Deep learning for sentence clustering in essay grading support

We describe and experimentally evaluate a method for automatically clustering
words according to their distribution in particular syntactic contexts.
Deterministic annealing is used to find lowest distortion sets of clusters. As
the annealing parameter increases, existing clusters become unstable and
subdivide, yielding a hierarchical ``soft'' clustering of the data. Clusters
are used as the basis for class models of word coocurrence, and the models
evaluated with respect to held-out test data.

该研究提出并实验评估了一种利用确定性退火方法将单词自动聚类的方法，使得聚类结果成为一种层次结构的软聚类，并将其用作词共现的类模型基础，然后利用测试数据进行了评估。