# Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction

arXiv: https://arxiv.org/abs/1604.04677 · HTML: https://mlsanity.com/paper/1604.04677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2016.

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

- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [Similarity-Distance-Magnitude Universal Verification](https://mlsanity.com/paper/2502.20167/) (2025)

## Abstract (arXiv)

We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that a character-based encoder-decoder model is particularly effective, outperforming other results on the AESW Shared Task on its own, and showing gains over a word-based counterpart. Our final model--a combination of three character-based encoder-decoder models, one word-based encoder-decoder model, and a sentence-level CNN--is the highest performing system on the AESW 2016 binary prediction Shared Task.