Writing Assistants and GEC

Datasets

• A New Dataset and Method for Automatically Grading ESOL Texts (Yannakoudakis et al., 2011)
• Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English (Dahlmeier et al., 2013)
• The CoNLL-2014 Shared Task on Grammatical Error Correction (Ng et al., 2014)
• JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction (Napoles et al., 2017)
• The BEA-2019 Shared Task on Grammatical Error Correction (Bryant et al., 2019)

Evaluation Metrics

• Better Evaluation for Grammatical Error Correction (Dahlmeier & Ng, 2012)
• Towards a standard evaluation method for grammatical error detection and correction (Felice & Briscoe, 2015)
• Ground Truth for Grammatical Error Correction Metrics (Napoles et al., 2015)
• There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction (Napoles et al., 2016)
• Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction (Bryant et al., 2017)
• Classifying Syntactic Errors in Learner Language (Choshen et al., 2020)

Evaluation Challenges & Limitations

• Human Evaluation of Grammatical Error Correction Systems (Grundkiewicz et al., 2015)
• Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality (Sakaguchi et al., 2016)
• Automatic Metric Validation for Grammatical Error Correction (Choshen & Abend, 2018)
• Neural Quality Estimation of Grammatical Error Correction (Chollampatt & Ng 2018)
• Inherent Biases in Reference-based Evaluation for Grammatical Error Correction (Choshen & Abend 2018)

Approaches: Statistical and Rule-Based

• Detection of Grammatical Errors Involving Prepositions (Chodorow et al., 2007)
• The Ups and Downs of Preposition Error Detection in ESL Writing (Tetreault & Chodorow, 2008)
• Grammatical Error Correction with Alternating Structure Optimization (Dahlmeier & Ng, 2011)
• Joint Learning and Inference for Grammatical Error Correction (Rozovskaya & Roth, 2013)
• Generalized Character-Level Spelling Error Correction (Farra et al., 2014)
• Grammatical error correction using hybrid systems and type filtering (Felice et al., 2014)
• Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction (Junczys-Dowmunt & Grundkiewicz, 2016)

Approaches: Sequence-to-Sequence

• Grammatical error correction using neural machine translation (Yuan & Briscoe, 2016)
• Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (Junczys-Dowmunt et al., 2018)
• Neural and FST-based approaches to grammatical error correction (Yuan et al., 2019)
• Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (Zhao et al., 2019)
• Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data (Grundkiewicz et al., 2019)
• Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model (Katsumata & Komachi, 2020)

Approaches: Text-Editing

• Parallel Iterative Edit Models for Local Sequence Transduction (Awasthi et al., 2019)
• Encode, Tag, Realize: High-Precision Text Editing (Malmi et al., 2019)
• Seq2Edits: Sequence Transduction Using Span-level Edit Operations (Stahlberg & Kumar, 2020)
• FELIX: Flexible Text Editing Through Tagging and Insertion (Mallinson et al., 2020)
• GECToR – Grammatical Error Correction: Tag, Not Rewrite (Omelianchuk et al., 2020)
• EdiT5: Semi-Autoregressive Text Editing with T5 Warm-Start (Mallinson et al., 2022)
• An Extended Sequence Tagging Vocabulary for Grammatical Error Correction (Mesham et al., 2023)

LLMs for GEC

• Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation (Fang et al., 2023)
• Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction (Coyne et al., 2023)
• ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark (Wu et al., 2023)
• Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods (Wu et al., 2023)
• CoEdIT: Text Editing by Task-Specific Instruction Tuning (Raheja et al., 2023)
• mEdIT: Multilingual Text Editing via Instruction Tuning (Raheja et al., 2024)
• Prompting open-source and commercial language models for grammatical error correction of English learner text (Davis et al., 2024)
• GPT-3.5 for Grammatical Error Correction (Katinskaia & Yangarber, 2024)
• Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models (Omelianchuk et al., 2024)
• Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction (Kobayashi et al., 2024)
• Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction (Kaneko & Okazaki, 2024)
• GMEG-EXP: A Dataset of Human- and LLM-Generated Explanations of Grammatical and Fluency Edits (López Cortez et al., 2024)
• Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond (Mita et al., 2024)