# Adapting Sequence Models for Sentence Correction

arXiv: https://arxiv.org/abs/1707.09067 · HTML: https://mlsanity.com/paper/1707.09067/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora](https://mlsanity.com/paper/2305.17906/) (2023)
- [An Extended Sequence Tagging Vocabulary for Grammatical Error Correction](https://mlsanity.com/paper/2302.05913/) (2023)
- [Similarity-Distance-Magnitude Universal Verification](https://mlsanity.com/paper/2502.20167/) (2025)

## Abstract (arXiv)

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.