---
title: "Attention-based Recurrent Convolutional Neural — Dong 2017"
description: "The paper addresses automatic essay scoring (AES), where a system must grade student essays in a way that agrees with human raters."
authors: ["Fei Dong", "Yue Zhang", "Jie Yang"]
year: 2017
venue: "Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)"
source: "https://aclanthology.org/K17-1017/"
doi: "10.18653/v1/k17-1017"
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/dong-2017-attention-based-recurrent-convolutional-neural/"
---
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# Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring

**Authors:** Fei Dong, [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/), [Jie Yang](https://md.research.elamur.ai/free/en/authors/jie-yang/) · **Year:** 2017 · **Venue:** Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/K17-1017/) · [PDF](https://aclanthology.org/K17-1017.pdf) · [DOI 10.18653/v1/k17-1017](https://doi.org/10.18653/v1/k17-1017) · [OpenAlex](https://openalex.org/W2740592764)

**Citations:** 277 citations (openalex-snapshot · 2026-10-14) · [88](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses automatic essay scoring (AES), where a system must grade student essays in a way that agrees with human raters. Earlier neural AES models encoded an essay as one vector using either an LSTM or a CNN. They never compared these two architectures under identical conditions, and they weighted all parts of the text equally. The authors build a hierarchical sentence-document model. A CNN with attention pooling turns each sentence's words into a sentence vector. An LSTM with a second attention-pooling layer then combines the sentence vectors into an essay representation, and a sigmoid layer produces the score. Word inputs can be either word embeddings or character-level CNN embeddings. Training minimizes mean squared error. Experiments on the ASAP essay data, evaluated with quadratic weighted kappa (QWK), show two things. CNNs work better at the sentence level, while LSTMs work better at the document level, which is plausibly a matter of local n-gram cues versus global coherence. Attention also brings significant gains. The full model reports the best published result on the ASAP test set. This matters because it gives feature-free, partly interpretable essay grading.

## Key points
- **Data and evaluation:** experiments use the ASAP (Automated Student Assessment Prize, Kaggle) essay data. The metric is quadratic weighted kappa, following the official competition criteria, and significance is checked with a one-tailed t-test.
- **Architecture:** the model builds on the two-level design of Dong and Zhang (2016). It has a sentence-level CNN with a window size of 5, so attention operates over 5-gram features, followed by a document-level LSTM. Attention pooling is applied at both the word/n-gram level and the sentence level.
- **Architecture comparison:** under matched settings, CNNs were found more suitable for modelling sentences and LSTMs for modelling whole documents.
- **Interpretability:** examples from Prompt 4 and Prompt 8 show how attention weights spread across sentences and n-grams. They illustrate which parts of an essay the model emphasizes.
- **Code release:** the code is publicly available at github.com/feidong1991/aes. Suggested future work is cross-domain 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.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cited by

Cited by

88 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 87 not yet.

- [How well can LLMs Grade Essays in Arabic?](https://md.research.elamur.ai/free/en/papers/ghazawi-2025-well-llms-grade-essays-arabic/)

## Cite

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

```bibtex
@inproceedings{dong2017attention,
  title = {{Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring}},
  author = {Dong, Fei and Zhang, Yue and Yang, Jie},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  year = {2017},
  doi = {10.18653/v1/k17-1017},
  url = {https://doi.org/10.18653/v1/k17-1017}
}
```

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