# Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors

arXiv: https://arxiv.org/abs/2108.12216 · HTML: https://mlsanity.com/paper/2108.12216/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://mlsanity.com/paper/2005.00987/) (2020)
- [How is BERT surprised? Layerwise detection of linguistic anomalies](https://mlsanity.com/paper/2105.07452/) (2021)
- [Semi-supervised Multitask Learning for Sequence Labeling](https://mlsanity.com/paper/1704.07156/) (2017)
- [Context is Key: Grammatical Error Detection with Contextual Word Representations](https://mlsanity.com/paper/1906.06593/) (2019)
- [Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection](https://mlsanity.com/paper/1904.07334/) (2019)
- [Do Grammatical Error Correction Models Realize Grammatical Generalization?](https://mlsanity.com/paper/2106.03031/) (2021)
- [Auxiliary Objectives for Neural Error Detection Models](https://mlsanity.com/paper/1707.05227/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check](https://mlsanity.com/paper/2307.13655/) (2023)
- [RILEC: Detection and Generation of L1 Russian Interference Errors in English Learner Texts](https://mlsanity.com/paper/2603.07366/) (2026)

## Abstract (arXiv)

In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to a non-language model-based method can achieve with the full training data; recall improves much faster with respect to training data size in the BERT-based method than in the non-language model method while precision behaves similarly. These suggest that (i) the BERT-based method should have a good knowledge of grammar required to recognize certain types of error and that (ii) it can transform the knowledge into error detection rules by fine-tuning with a few training samples, which explains its high generalization ability in grammatical error detection. We further show with pseudo error data that it actually exhibits such nice properties in learning rules for recognizing various types of error. Finally, based on these findings, we explore a cost-effective method for detecting grammatical errors with feedback comments explaining relevant grammatical rules to learners.