====== Grammatical Error Correction ======
Also known as "automatic grammar checking."

===== Overviews =====
  * [[https://www.cambridge.org/core/services/aop-cambridge-core/content/view/E1B54FD65963E65D46EF440B5A13F186/S1351324921000164a.pdf/div-class-title-the-automated-writing-assistance-landscape-in-2021-div.pdf|Dale & Viethen 2021 - The Automated Writing Assistance Landscape in 2021]]

===== Papers =====
  * [[https://arxiv.org/pdf/1804.05940.pdf|Junczys-Dowmunt et al 2017 - Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task]]
  * [[https://www.aclweb.org/anthology/W19-4427.pdf|Grundkiewicz et al 2019 - Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data]]
  * [[https://arxiv.org/pdf/1904.05780.pdf|Lichtarge et al 2019 - Corpora Generation for Grammatical Error Correction]] Paper by Google, deployed in Google Docs. [[https://cloud.google.com/blog/products/productivity-collaboration/using-neural-machine-translation-to-correct-grammatical-in-google-docs|Blog post]]
  * [[https://arxiv.org/pdf/1906.01733.pdf|Alikaniotis & Raheja et al 2019 - The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction]] Paper by Grammarly. [[https://www.grammarly.com/blog/engineering/under-the-hood-at-grammarly-leveraging-transformer-language-models-for-grammatical-error-correction/|Blog post]]

===== Detecting Ungrammatical Sentences (Linguistic Acceptability) =====
  * [[https://arxiv.org/pdf/1805.12471.pdf|Warstadt et al 2018 - Neural Network Acceptability Judgments]]

===== Datasets =====
  * Linguistic Acceptability
    * [[https://arxiv.org/pdf/1805.12471.pdf|Corpus of Linguistic Acceptability (CoLa)]] [[https://nyu-mll.github.io/CoLA/|dataset]]

===== Workshops ======
  * [[https://sig-edu.org/bea/current|Workshop on Innovative Use of NLP for Building Educational Applications]]