# Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation arXiv: https://arxiv.org/abs/2205.10884 ยท HTML: https://mlsanity.com/paper/2205.10884/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 40 references that are in the map. - First posted on arXiv 22 May 2022. ## Builds on (most cited first, 20 of 20) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015) - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019) - [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018) - [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019) - [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020) - [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016) - [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019) - [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019) - [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://mlsanity.com/paper/2005.00987/) (2020) - [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020) - [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://mlsanity.com/paper/1804.05940/) (2018) - [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://mlsanity.com/paper/1909.00502/) (2019) - [Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data](https://mlsanity.com/paper/1903.00138/) (2019) - [ASR Error Correction and Domain Adaptation Using Machine Translation](https://mlsanity.com/paper/2003.07692/) (2020) - [Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction](https://mlsanity.com/paper/2010.03260/) (2020) - [Improving Grammatical Error Correction with Machine Translation Pairs](https://mlsanity.com/paper/1911.02825/) (2019) ## Built on by (most cited first, 15 of 15) - [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.10442/) (2022) - [GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning](https://mlsanity.com/paper/2307.13923/) (2023) - [Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction](https://mlsanity.com/paper/2306.17447/) (2023) - [Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction](https://mlsanity.com/paper/2211.13252/) (2022) - [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023) - [Non-autoregressive Text Editing with Copy-aware Latent Alignments](https://mlsanity.com/paper/2310.07821/) (2023) - [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022) - [Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector](https://mlsanity.com/paper/2402.04601/) (2024) - [Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2501.00334/) (2024) - [LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2403.17413/) (2024) - [Text Editing as Imitation Game](https://mlsanity.com/paper/2210.12276/) (2022) - [Detection-Correction Structure via General Language Model for Grammatical Error Correction](https://mlsanity.com/paper/2405.17804/) (2024) - [System Report for CCL24-Eval Task 7: Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation](https://mlsanity.com/paper/2407.08206/) (2024) - [Multi-head Sequence Tagging Model for Grammatical Error Correction](https://mlsanity.com/paper/2410.16473/) (2024) - [Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2606.15416/) (2026) ## Abstract (arXiv) The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key principles of GEC is to keep the correct parts unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq) models generate results from scratch, which are not guaranteed to follow the original sentence structure and may suffer from the over-correction problem. In the meantime, the recently proposed sequence tagging models can overcome the over-correction problem by only generating edit operations, but are conditioned on human designed language-specific tagging labels. In this paper, we combine the pros and alleviate the cons of both models by proposing a novel Sequence-to-Action~(S2A) module. The S2A module jointly takes the source and target sentences as input, and is able to automatically generate a token-level action sequence before predicting each token, where each action is generated from three choices named SKIP, COPY and GENerate. Then the actions are fused with the basic seq2seq framework to provide final predictions. We conduct experiments on the benchmark datasets of both English and Chinese GEC tasks. Our model consistently outperforms the seq2seq baselines, while being able to significantly alleviate the over-correction problem as well as holding better generality and diversity in the generation results compared to the sequence tagging models.