# Deep learning for sentence clustering in essay grading support

arXiv: https://arxiv.org/abs/2104.11556 · HTML: https://mlsanity.com/paper/2104.11556/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [Mining Discourse Markers for Unsupervised Sentence Representation Learning](https://mlsanity.com/paper/1903.11850/) (2019)
- [Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks](https://mlsanity.com/paper/1902.00972/) (2019)
- [My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism](https://mlsanity.com/paper/2012.13872/) (2020)
- [Clustering students' open-ended questionnaire answers](https://mlsanity.com/paper/1809.07306/) (2018)

## Built on by (most cited first, 3 of 3)

- [AugCSE: Contrastive Sentence Embedding with Diverse Augmentations](https://mlsanity.com/paper/2210.13749/) (2022)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)
- [Towards Theme Detection in Personal Finance Questions](https://mlsanity.com/paper/2110.01550/) (2021)

## Abstract (arXiv)

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.