# Compositional Sequence Labeling Models for Error Detection in Learner Writing

arXiv: https://arxiv.org/abs/1607.06153 · HTML: https://mlsanity.com/paper/1607.06153/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2016.

## Built on by (most cited first, 16 of 16)

- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [What to Pre-Train on? Efficient Intermediate Task Selection](https://mlsanity.com/paper/2104.08247/) (2021)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
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- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers](https://mlsanity.com/paper/2103.14465/) (2021)
- [Oddballness: universal anomaly detection with language models](https://mlsanity.com/paper/2409.03046/) (2024)
- [Combining GCN and Transformer for Chinese Grammatical Error Detection](https://mlsanity.com/paper/2105.09085/) (2021)
- [Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers](https://mlsanity.com/paper/2303.07991/) (2023)
- [Detection-Correction Structure via General Language Model for Grammatical Error Correction](https://mlsanity.com/paper/2405.17804/) (2024)
- [Development of an Extractive Title Generation System Using Titles of Papers of Top Conferences for Intermediate English Students](https://mlsanity.com/paper/2110.04204/) (2021)
- [Approximate Conditional Coverage & Calibration via Neural Model Approximations](https://mlsanity.com/paper/2205.14310/) (2022)
- [Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection](https://mlsanity.com/paper/2407.11854/) (2024)

## Abstract (arXiv)

In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators.