---
title: "Through the Sentence Lens — paper by Melanchthon et al. (2026)"
description: "Automated Essay Scoring (AES) usually returns one score per essay. 43 references. Among them: 0."
authors: ["Daniel Mora Melanchthon", "Stefan Keller", "Andrea Horbach"]
year: 2026
venue: "Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)"
source: "https://aclanthology.org/2026.bea-1.16/"
doi: "10.18653/v1/2026.bea-1.16"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/melanchthon-2026-through-sentence-lens-explainable-essay/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/melanchthon-2026-through-sentence-lens-explainable-essay/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/45/) / Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions

# Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions

**Authors:** [Daniel Mora Melanchthon](https://md.research.elamur.ai/free/en/authors/daniel-mora-melanchthon/), Stefan Keller and [Andrea Horbach](https://md.research.elamur.ai/free/en/authors/andrea-horbach/) · **Year:** 2026 · **Venue:** [Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)](https://md.research.elamur.ai/free/en/venues/bea/) ([BEA 2026](https://md.research.elamur.ai/free/en/venues/bea/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.bea-1.16/) · [PDF](https://aclanthology.org/2026.bea-1.16.pdf) · [DOI 10.18653/v1/2026.bea-1.16](https://doi.org/10.18653/v1/2026.bea-1.16) · [OpenAlex](https://openalex.org/W7166894766)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Automated Essay Scoring (AES) usually returns one score per essay. That gives teachers and learners little insight into how the score was reached. Existing explanation techniques, such as gradient attribution or feature importance, tend to be unintuitive or too complex for classroom use. The authors instead aim to score individual sentences. No sentence-level gold labels exist, so they derive pseudo scores by ablation: an essay-level model scores each essay with and without a given sentence, and the change in prediction serves as that sentence's score. Sentence-level models are then trained on these pseudo scores. Three architectures are tested: linear regression on linguistic features, a fine-tuned DistilBERT model, and a hybrid of the two. The traits are content and language quality, using ASAP/ASAP++ and the L2 English MEWS corpus. Averaged sentence predictions are compared with essay-level baselines, and the sentence outputs are checked against human annotations. The results show a trade-off between essay-level accuracy and sentence-level granularity. Language quality works better than content, and content works mainly on prompts with shorter texts. The work matters because it points to feedback at the sentence level that learners can act on when revising.

## Key points

- **Data:** ASAP prompts with ASAP++ trait annotations (native English writers, grades 7–10) and the MEWS prompts TE and AD, plus their combination TEAD (German-L1 upper-secondary learners in Germany and Switzerland). Holistic and organisation traits were excluded as document-level.
- **Models:** a linear regressor using 220 linguistic features, a DistilBERT-based "LLM" model, and a Hybrid model that concatenates both representations.
- **Essay-level comparison:** the best sentence-level LR model matched its essay-level baseline on 13 of 18 prompt settings.
- **Human annotation:** five annotators judged the polarity of sentences in a MEWS subset for language quality. Agreement was low (Krippendorff's alpha 0.17; pairwise Cohen's kappa 0.14–0.22).
- **Human–model correlation:** Spearman correlations with human judgments were weak for sentence-level predictions: about 0.30 for the LLM model, 0.19 for Hybrid, and −0.15 for LR. Code is available at github.com/melanchthon19/sentence-level-aes.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Christian-Albrechts-Universität zu Kiel](https://md.research.elamur.ai/free/en/universities/christian-albrechts-universitat-zu-kiel/) — University, Kiel, Germany · 6 works · 38,870 citations
- [Zurich University of Teacher Education](https://md.research.elamur.ai/free/en/universities/zurich-university-of-teacher-education/) — University, Zurich, Switzerland · 2 works · 5 citations
- [Leibniz Institute for Science and Mathematics Education](https://md.research.elamur.ai/free/en/labs/leibniz-institute-for-science-and-mathematics-education/) — Research institute, Kiel, Germany · 4 works · 261 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works

🧭 Topics (1): [🖍️ Grammatical error correction & learner language](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · 156 works

## Cite

Cite as: Daniel Mora Melanchthon, Stefan Keller and Andrea Horbach. 2026. Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions. Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{melanchthon2026through,
  title = {{Through the Sentence Lens: Explainable Essay Scoring through Fine-Grained Predictions}},
  author = {Daniel Mora Melanchthon, Stefan Keller and Andrea Horbach},
  booktitle = {Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.bea-1.16},
  url = {https://doi.org/10.18653/v1/2026.bea-1.16}
}
```

- [← previous Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek](https://md.research.elamur.ai/free/en/papers/sakunkoo-2026-through-looking-glass-multilingual-ai/)
- [next → TiC-MuFormer: Time-Aware Caption-Integrated Multimodal Transformers for User-Level Mental Health Modeling](https://md.research.elamur.ai/free/en/papers/tsoumplekas-2026-tic-muformer-time-aware-caption/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/45/)

## References

43 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Dimitrios Alikaniotis, Helen Yannakoudakis, and Marek Rei. 2016. [Automatic text scoring using neural networks](https://md.research.elamur.ai/free/en/papers/alikaniotis-2016-automatic-text-scoring-neural-networks/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 715–725, Berlin, Germany. Association for Computational Linguistics…
2. Øistein E. Andersen, Helen Yannakoudakis, Fiona Barker, and Tim Parish. 2013. Developing and testing a self-assessment and tutoring system. In Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications, pages 32–41, Atlanta, Georgia. Association for Computation… [find](https://scholar.google.com/scholar?q=%C3%98istein+E%2E+Andersen%2C+Helen+Yannakoudakis%2C+Fiona+Barker%2C+and+Tim+Parish%2E+2013%2E+Developing+and+testing+a+self%2Dassessment+and+tutoring+system%2E+In+Proceedings+of+the+Eighth+Workshop+on+Innovative+Use+of+N)
3. Pablo Barceló, Mikaël Monet, Jorge Pérez, and Bernardo Subercaseaux. 2020. Model interpretability through the lens of computational complexity. In Advances in Neural Information Processing Systems, volume 33, pages 15487–15498. Curran Associates, Inc. [find](https://scholar.google.com/scholar?q=Pablo+Barcel%C3%B3%2C+Mika%C3%ABl+Monet%2C+Jorge+P%C3%A9rez%2C+and+Bernardo+Subercaseaux%2E+2020%2E+Model+interpretability+through+the+lens+of+computational+complexity%2E+In+Advances+in+Neural+Information+Processing+Systems%2C+vo)
4. Beata Beigman Klebanov and Nitin Madnani. 2020. Automated evaluation of writing – 50 years and counting. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7796–7810, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Beata+Beigman+Klebanov+and+Nitin+Madnani%2E+2020%2E+Automated+evaluation+of+writing+%E2%80%93+50+years+and+counting%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pag)
5. Marie Bexte, Andrea Horbach, and Torsten Zesch. 2024. Strengths and weaknesses of automated scoring of free-text student answers. Informatik Spektrum, 47(3):78–86. [find](https://scholar.google.com/scholar?q=Marie+Bexte%2C+Andrea+Horbach%2C+and+Torsten+Zesch%2E+2024%2E+Strengths+and+weaknesses+of+automated+scoring+of+free%2Dtext+student+answers%2E+Informatik+Spektrum%2C+47%283%29%3A78%E2%80%9386%2E)
6. Hila Chefer, Shir Gur, and Lior Wolf. 2021. Transformer interpretability beyond attention visualization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 782–791. [find](https://scholar.google.com/scholar?q=Hila+Chefer%2C+Shir+Gur%2C+and+Lior+Wolf%2E+2021%2E+Transformer+interpretability+beyond+attention+visualization%2E+In+2021+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29%2C+pages+782%E2%80%93791%2E)
7. J Cohen. 1968. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychological bulletin, 70(4):213—220. [find](https://scholar.google.com/scholar?q=J+Cohen%2E+1968%2E+Weighted+kappa%3A+nominal+scale+agreement+with+provision+for+scaled+disagreement+or+partial+credit%2E+Psychological+bulletin%2C+70%284%29%3A213%E2%80%94220%2E)
8. Zahra Dehghanighobadi, Asja Fischer, and Muhammad Bilal Zafar. 2025. Can LLMs Explain Themselves Counterfactually? In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 7787–7815, Suzhou, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Zahra+Dehghanighobadi%2C+Asja+Fischer%2C+and+Muhammad+Bilal+Zafar%2E+2025%2E+Can+LLMs+Explain+Themselves+Counterfactually%3F+In+Proceedings+of+the+2025+Conference+on+Empirical+Methods+in+Natural+Language+Proces)
9. Fei Dong, Yue Zhang, and Jie Yang. 2017. [Attention-based recurrent convolutional neural network for automatic essay scoring](https://md.research.elamur.ai/free/en/papers/dong-2017-attention-based-recurrent-convolutional-neural/). In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 153–162, Vancouver, Canada. Association for Computational Linguistics.
10. Finale Doshi-Velez and Been Kim. 2017. [Towards a rigorous science of interpretable machine learning](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/). Preprint, arXiv:1702.08608.
11. Sohaila Eltanbouly, Salam Albatarni, and Tamer Elsayed. 2025. TRATES: Trait-specific rubric-assisted cross-prompt essay scoring. In Findings of the Association for Computational Linguistics: ACL 2025, pages 20528–20543, Vienna, Austria. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sohaila+Eltanbouly%2C+Salam+Albatarni%2C+and+Tamer+Elsayed%2E+2025%2E+TRATES%3A+Trait%2Dspecific+rubric%2Dassisted+cross%2Dprompt+essay+scoring%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2025%2C)
12. Erin Hall, Mohammed Seyam, and Daniel Dunlap. 2024. Exploring explainability and transparency in automated essay scoring systems: A user-centered evaluation. In Learning and Collaboration Technologies, pages 266–282, Cham. Springer Nature Switzerland. [find](https://scholar.google.com/scholar?q=Erin+Hall%2C+Mohammed+Seyam%2C+and+Daniel+Dunlap%2E+2024%2E+Exploring+explainability+and+transparency+in+automated+essay+scoring+systems%3A+A+user%2Dcentered+evaluation%2E+In+Learning+and+Collaboration+Technologies)
13. Matthew Honnibal, Ines Montani, Sofie Van Lan-deghem, and Adriane Boyd. 2020. spacy: Industrial-strength natural language processing in python. [find](https://scholar.google.com/scholar?q=Matthew+Honnibal%2C+Ines+Montani%2C+Sofie+Van+Lan%2Ddeghem%2C+and+Adriane+Boyd%2E+2020%2E+spacy%3A+Industrial%2Dstrength+natural+language+processing+in+python%2E)
14. Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilė Lukošiūtė, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, an… — [Measuring Faithfulness in Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/)
15. Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ninghao Liu, and Xiaoming Zhai. 2024. Applying large language models and chain-of-thought for automatic scoring. Computers and Education: Artificial Intelligence, 6:100213. [find](https://scholar.google.com/scholar?q=Gyeong%2DGeon+Lee%2C+Ehsan+Latif%2C+Xuansheng+Wu%2C+Ninghao+Liu%2C+and+Xiaoming+Zhai%2E+2024%2E+Applying+large+language+models+and+chain%2Dof%2Dthought+for+automatic+scoring%2E+Computers+and+Education%3A+Artificial+Intelli)
16. Mehadi Hossain and Hossen A Mustafa. 2023. Automated writing evaluation using sentence by sentence scoring model. In 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), pages 1–6. [find](https://scholar.google.com/scholar?q=Mehadi+Hossain+and+Hossen+A+Mustafa%2E+2023%2E+Automated+writing+evaluation+using+sentence+by+sentence+scoring+model%2E+In+2023+International+Conference+on+Next%2DGeneration+Computing%2C+IoT+and+Machine+Learnin)
17. Shengjie Li and Vincent Ng. 2024. Conundrums in cross-prompt automated essay scoring: Making sense of the state of the art. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7661– 7681, Bangkok, Thailand. Association for Computa… [find](https://scholar.google.com/scholar?q=Shengjie+Li+and+Vincent+Ng%2E+2024%2E+Conundrums+in+cross%2Dprompt+automated+essay+scoring%3A+Making+sense+of+the+state+of+the+art%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computation)
18. Sungho Jeon and Michael Strube. 2021. Countering the influence of essay length in neural essay scoring. In Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing, pages 32–38, Virtual. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sungho+Jeon+and+Michael+Strube%2E+2021%2E+Countering+the+influence+of+essay+length+in+neural+essay+scoring%2E+In+Proceedings+of+the+Second+Workshop+on+Simple+and+Efficient+Natural+Language+Processing%2C+pages)
19. Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, and Dejing Dou. 2022. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12):3197–3234. [find](https://scholar.google.com/scholar?q=Xuhong+Li%2C+Haoyi+Xiong%2C+Xingjian+Li%2C+Xuanyu+Wu%2C+Xiao+Zhang%2C+Ji+Liu%2C+Jiang+Bian%2C+and+Dejing+Dou%2E+2022%2E+Interpretable+deep+learning%3A+interpretation%2C+interpretability%2C+trustworthiness%2C+and+beyond%2E+Knowle)
20. Stefan D. Keller, Johanna Fleckenstein, Maleika Krüger, Olaf Köller, and André A. Rupp. 2020. English writing skills of students in upper secondary education: Results from an empirical study in switzerland and germany. Journal of Second Language Writing, 48:100700. [find](https://scholar.google.com/scholar?q=Stefan+D%2E+Keller%2C+Johanna+Fleckenstein%2C+Maleika+Kr%C3%BCger%2C+Olaf+K%C3%B6ller%2C+and+Andr%C3%A9+A%2E+Rupp%2E+2020%2E+English+writing+skills+of+students+in+upper+secondary+education%3A+Results+from+an+empirical+study+in+switze)
21. Zachary C. Lipton. 2018. The mythos of model interpretability. Commun. ACM, 61(10):36–43. [find](https://scholar.google.com/scholar?q=Zachary+C%2E+Lipton%2E+2018%2E+The+mythos+of+model+interpretability%2E+Commun%2E+ACM%2C+61%2810%29%3A36%E2%80%9343%2E)
22. Stefan D. Keller, Julian Lohmann, Ruth Trüb, Johanna Fleckenstein, Jennifer Meyer, Thorben Jansen, and Jens Möller. 2024. Language quality, content, structure: What analytic ratings tell us about efl writing skills at upper secondary school level in germany and switzerland. Journal of Second Languag… [find](https://scholar.google.com/scholar?q=Stefan+D%2E+Keller%2C+Julian+Lohmann%2C+Ruth+Tr%C3%BCb%2C+Johanna+Fleckenstein%2C+Jennifer+Meyer%2C+Thorben+Jansen%2C+and+Jens+M%C3%B6ller%2E+2024%2E+Language+quality%2C+content%2C+structure%3A+What+analytic+ratings+tell+us+about+efl+)
23. Vivekanandan Kumar and David Boulanger. 2020. Explainable automated essay scoring: Deep learning really has pedagogical value. Frontiers in Education, 5. [find](https://scholar.google.com/scholar?q=Vivekanandan+Kumar+and+David+Boulanger%2E+2020%2E+Explainable+automated+essay+scoring%3A+Deep+learning+really+has+pedagogical+value%2E+Frontiers+in+Education%2C+5%2E)
24. Vivekanandan S. Kumar and David Boulanger. 2021. Automated essay scoring and the deep learning black box: How are rubric scores determined? International Journal of Artificial Intelligence in Education, 31(3):538–584. [find](https://scholar.google.com/scholar?q=Vivekanandan+S%2E+Kumar+and+David+Boulanger%2E+2021%2E+Automated+essay+scoring+and+the+deep+learning+black+box%3A+How+are+rubric+scores+determined%3F+International+Journal+of+Artificial+Intelligence+in+Educatio)
25. Yaman Kumar, Swati Aggarwal, Debanjan Mahata, Rajiv Ratn Shah, Ponnurangam Kumaraguru, and Roger Zimmermann. 2019. Get it scored using autosas — an automated system for scoring short answers. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Ap… [find](https://scholar.google.com/scholar?q=Yaman+Kumar%2C+Swati+Aggarwal%2C+Debanjan+Mahata%2C+Rajiv+Ratn+Shah%2C+Ponnurangam+Kumaraguru%2C+and+Roger+Zimmermann%2E+2019%2E+Get+it+scored+using+autosas+%E2%80%94+an+automated+system+for+scoring+short+answers%2E+In+Proce)
26. Julian F. Lohmann, Fynn Junge, Jens Möller, Johanna Fleckenstein, Ruth Trüb, Stefan Keller, Thorben Jansen, and Andrea Horbach. 2024. Neural networks or linguistic features? - comparing different machine-learning approaches for automated assessment of text quality traits among l1- and l2-learners’ a… [find](https://scholar.google.com/scholar?q=Julian+F%2E+Lohmann%2C+Fynn+Junge%2C+Jens+M%C3%B6ller%2C+Johanna+Fleckenstein%2C+Ruth+Tr%C3%BCb%2C+Stefan+Keller%2C+Thorben+Jansen%2C+and+Andrea+Horbach%2E+2024%2E+Neural+networks+or+linguistic+features%3F+%2D+comparing+different+mach)
27. Andreas Madsen, Siva Reddy, and Sarath Chandar. 2022. Post-hoc interpretability for neural nlp: A survey. ACM Comput. Surv., 55(8). [find](https://scholar.google.com/scholar?q=Andreas+Madsen%2C+Siva+Reddy%2C+and+Sarath+Chandar%2E+2022%2E+Post%2Dhoc+interpretability+for+neural+nlp%3A+A+survey%2E+ACM+Comput%2E+Surv%2E%2C+55%288%29%2E)
28. Watheq Ahmad Mansour, Salam Albatarni, Sohaila Eltanbouly, and Tamer Elsayed. 2024. Can large language models automatically score proficiency of written essays? In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024… [find](https://scholar.google.com/scholar?q=Watheq+Ahmad+Mansour%2C+Salam+Albatarni%2C+Sohaila+Eltanbouly%2C+and+Tamer+Elsayed%2E+2024%2E+Can+large+language+models+automatically+score+proficiency+of+written+essays%3F+In+Proceedings+of+the+2024+Joint+Intern)
29. Sandeep Mathias and Pushpak Bhattacharyya. 2018. ASAP++: Enriching the ASAP automated essay grading dataset with essay attribute scores. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association… [find](https://scholar.google.com/scholar?q=Sandeep+Mathias+and+Pushpak+Bhattacharyya%2E+2018%2E+ASAP%2B%2B%3A+Enriching+the+ASAP+automated+essay+grading+dataset+with+essay+attribute+scores%2E+In+Proceedings+of+the+Eleventh+International+Conference+on+Lang)
30. Atsushi Mizumoto and Masaki Eguchi. 2023. Exploring the potential of using an ai language model for automated essay scoring. Research Methods in Applied Linguistics, 2(2):100050. [find](https://scholar.google.com/scholar?q=Atsushi+Mizumoto+and+Masaki+Eguchi%2E+2023%2E+Exploring+the+potential+of+using+an+ai+language+model+for+automated+essay+scoring%2E+Research+Methods+in+Applied+Linguistics%2C+2%282%29%3A100050%2E)
31. Tomoya Mizumoto, Hiroki Ouchi, Yoriko Isobe, Paul Reisert, Ryo Nagata, Satoshi Sekine, and Kentaro Inui. 2019. Analytic score prediction and justification identification in automated short answer scoring. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational App… [find](https://scholar.google.com/scholar?q=Tomoya+Mizumoto%2C+Hiroki+Ouchi%2C+Yoriko+Isobe%2C+Paul+Reisert%2C+Ryo+Nagata%2C+Satoshi+Sekine%2C+and+Kentaro+Inui%2E+2019%2E+Analytic+score+prediction+and+justification+identification+in+automated+short+answer+scor)
32. Christoph Molnar. 2025. Interpretable Machine Learning, 3 edition. [find](https://scholar.google.com/scholar?q=Christoph+Molnar%2E+2025%2E+Interpretable+Machine+Learning%2C+3+edition%2E)
33. Swapnil Parekh, Yaman Kumar Singla, Changyou Chen, Junyi Jessy Li, and Rajiv Ratn Shah. 2020. My teacher thinks the world is flat! interpreting automatic essay scoring mechanism. Preprint, arXiv:2012.13872. — [arXiv:2012.13872](https://arxiv.org/abs/2012.13872) [find](https://scholar.google.com/scholar?q=Swapnil+Parekh%2C+Yaman+Kumar+Singla%2C+Changyou+Chen%2C+Junyi+Jessy+Li%2C+and+Rajiv+Ratn+Shah%2E+2020%2E+My+teacher+thinks+the+world+is+flat%21+interpreting+automatic+essay+scoring+mechanism%2E+Preprint%2C+arXiv%3A2012%2E)
34. Dadi Ramesh and Suresh Kumar Sanampudi. 2022. An automated essay scoring systems: a systematic literature review. Artificial Intelligence Review, 55(3):2495–2527. language models with combination of regression and ranking. In Findings of the Association for Computational Linguistics: EMNLP 2020, pag… [find](https://scholar.google.com/scholar?q=Dadi+Ramesh+and+Suresh+Kumar+Sanampudi%2E+2022%2E+An+automated+essay+scoring+systems%3A+a+systematic+literature+review%2E+Artificial+Intelligence+Review%2C+55%283%29%3A2495%E2%80%932527%2E+language+models+with+combination+of+r)
35. Haneul Yoo, Jieun Han, So-Yeon Ahn, and Alice Oh. 2025. DREsS: Dataset for rubric-based essay scoring on EFL writing. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13439– 13454, Vienna, Austria. Association for Computational… [find](https://scholar.google.com/scholar?q=Haneul+Yoo%2C+Jieun+Han%2C+So%2DYeon+Ahn%2C+and+Alice+Oh%2E+2025%2E+DREsS%3A+Dataset+for+rubric%2Dbased+essay+scoring+on+EFL+writing%2E+In+Proceedings+of+the+63rd+Annual+Meeting+of+the+Association+for+Computational+Lin)
36. Lui Yoshida. 2025. Are the Reasoning Models Good at Automated Essay Scoring? In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 8388–8394, Suzhou, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Lui+Yoshida%2E+2025%2E+Are+the+Reasoning+Models+Good+at+Automated+Essay+Scoring%3F+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2025%2C+pages+8388%E2%80%938394%2C+Suzhou%2C+China%2E+Association+for+C)
37. Cynthia Rudin. 2019. [Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/). Nature Machine Intelligence, 1(5):206–215.
38. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. Preprint, arXiv:1910.01108. [find](https://scholar.google.com/scholar?q=Victor+Sanh%2C+Lysandre+Debut%2C+Julien+Chaumond%2C+and+Thomas+Wolf%2E+2019%2E+Distilbert%2C+a+distilled+version+of+bert%3A+smaller%2C+faster%2C+cheaper+and+lighter%2E+Preprint%2C+arXiv%3A1910%2E01108%2E)
39. Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2023. [Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/). In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA. C…
40. Felipe Urrutia, Cristian Buc, Roberto Araya, and Valentin Barriere. 2025. Unsupervised automatic short answer grading and essay scoring: A weakly supervised explainable approach. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), pages 38–… [find](https://scholar.google.com/scholar?q=Felipe+Urrutia%2C+Cristian+Buc%2C+Roberto+Araya%2C+and+Valentin+Barriere%2E+2025%2E+Unsupervised+automatic+short+answer+grading+and+essay+scoring%3A+A+weakly+supervised+explainable+approach%2E+In+Proceedings+of+the)
41. Bronwyn Woods, David Adamson, Shayne Miel, and Elijah Mayfield. 2017. Formative essay feedback using predictive scoring models. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, page 2071–2080, New York, NY, USA. Association for Computing… [find](https://scholar.google.com/scholar?q=Bronwyn+Woods%2C+David+Adamson%2C+Shayne+Miel%2C+and+Elijah+Mayfield%2E+2017%2E+Formative+essay+feedback+using+predictive+scoring+models%2E+In+Proceedings+of+the+23rd+ACM+SIGKDD+International+Conference+on+Knowle)
42. Wenbo Xu, Rohana Mahmud, and Wai Lam Hoo. 2024. A systematic literature review: Are automated essay scoring systems competent in real-life education scenarios? IEEE Access, 12:77639–77657. [find](https://scholar.google.com/scholar?q=Wenbo+Xu%2C+Rohana+Mahmud%2C+and+Wai+Lam+Hoo%2E+2024%2E+A+systematic+literature+review%3A+Are+automated+essay+scoring+systems+competent+in+real%2Dlife+education+scenarios%3F+IEEE+Access%2C+12%3A77639%E2%80%9377657%2E)
43. Ruosong Yang, Jiannong Cao, Zhiyuan Wen, Youzheng Wu, and Xiaodong He. 2020. Enhancing automated essay scoring performance via fine-tuning pre-trained [find](https://scholar.google.com/scholar?q=Ruosong+Yang%2C+Jiannong+Cao%2C+Zhiyuan+Wen%2C+Youzheng+Wu%2C+and+Xiaodong+He%2E+2020%2E+Enhancing+automated+essay+scoring+performance+via+fine%2Dtuning+pre%2Dtrained)

*Updated 1 November 2026*