Papers with Grammatical error correction
Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that the performance of large language models is insufficient for non-English data, such as Korean. |
| Approach: | They propose a framework that integrates evidential information from external sources into the prompt for the Korean GEC task. |
| Outcome: | The proposed framework extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases. |
Grammatical Error Correction Systems for Automated Assessment: Are They Susceptible to Universal Adversarial Attacks? (2022.aacl-main)
Copied to clipboard
| Challenge: | With advances in deep learning, GEC systems are susceptible to adversarial attacks, in which a small change at the input can cause large undesired changes at the output. |
| Approach: | They propose to use a concatenative universal attack to deceive the system into not correcting grammatical errors to create the perception of higher language ability. |
| Outcome: | The proposed attack can deceive the system into not correcting (concealing) grammatical errors to create the perception of higher language ability. |
Comparison of Grammatical Error Correction Using Back-Translation Models (2021.naacl-srw)
Copied to clipboard
| Challenge: | Currently, a mainstream approach to generate pseudo data is back-translation (BT). |
| Approach: | They propose to use back-translation to generate pseudo data that contains grammatical and ungrammatically produced sentences. |
| Outcome: | The proposed methods improve or interpolate the performance of each error type compared with a single BT model with different seeds. |
Grammatical Error Correction in Low-Resource Scenarios (D19-55)
Copied to clipboard
| Challenge: | Existing systems for grammatical error correction in English have been limited . however, there is limited progress on error correction of other languages . |
| Approach: | They propose a dataset on grammatical error correction for Czech and an annotated learner corpus for Russian and Czech. |
| Outcome: | The proposed model can reach new state-of-the-art on Czech, German and Russian datasets. |
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding (2023.acl-long)
Copied to clipboard
| Challenge: | grammatical error correction is an important NLP task that is usually solved with autoregressive sequence-to-sequence models. |
| Approach: | They propose a non-autoregressive approach to grammatical error correction that decouples a permutation network and a decoder network that fills in specific tokens. |
| Outcome: | The proposed approach improves over previously known non-autoregressive methods and reaches the level of autoregressive approaches that do not use language-specific synthetic data generation methods. |
Detection-Correction Structure via General Language Model for Grammatical Error Correction (2024.acl-long)
Copied to clipboard
| Challenge: | Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits. |
| Approach: | They propose a detection-correction structure based on the general language model which integrates detection and correction into a single model. |
| Outcome: | The proposed model outperforms the state-of-the-art models on English and Chinese datasets. |
COCOGEC: Counterfactual Generation for Robust Grammatical Error Correction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing GEC models fail to understand error patterns in varying contexts . a framework that generates copies of training instances with error-irrelevant contexts altered is proposed . |
| Approach: | They propose a framework that generates copies of training instances with error-irrelevant contexts altered. |
| Outcome: | The proposed framework outperforms baselines on the simulated tasks and outperformed existing models. |
Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction (2025.naacl-long)
Copied to clipboard
| Challenge: | Grammatical error correction (GEC) aims to correct grammatical, spelling, and semantic errors in natural language text. |
| Approach: | They propose a retrieval method based on natural language grammatical error explanations to match inputs with pre-constructed databases where explanations for erroneous samples are generated by LLMs. |
| Outcome: | The proposed method outperforms existing semantic and BM25-based retrieval techniques without additional training or language adaptation. |
Neural Quality Estimation of Grammatical Error Correction (D18-1)
Copied to clipboard
| Challenge: | Grammatical error correction systems are expected to correct most learners’ writing errors, but in practice they often produce spurious corrections and fail to correct many errors, thereby misleading learners. |
| Approach: | They propose to use supervised learning to estimate the quality of GEC output sentences to help instructors decide whether to correct the errors or ignore them altogether. |
| Outcome: | The proposed model improves on a feature-based baseline and shows that the state-of-the-art system can be improved when quality scores are used as features for re-ranking the N-best candidates. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
| Approach: | They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information. |
| Outcome: | The proposed framework outperforms existing methods on various datasets by a clear margin. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
| Approach: | They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input. |
| Outcome: | The proposed method can perform human-in-the-loop error correction tasks. |
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on grammatical error correction (GEC) in morphologically rich languages have been limited due to data scarcity and language complexity. |
| Approach: | They propose to use Arabic GEC to improve performance across three datasets . they define Arabic grammatical error detection task as auxiliary input . |
| Outcome: | The proposed models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. |
Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora (2023.acl-long)
Copied to clipboard
Svanhvít Lilja Ingólfsdóttir, Petur Ragnarsson, Haukur Jónsson, Haukur Simonarson, Vilhjalmur Thorsteinsson, Vésteinn Snæbjarnarson
| Challenge: | Spelling mistakes due to typos and rushed writing, nonstandard punctuation and spelling, and grammatical and stylistic issues are common to almost everyone who writes any kind of text. |
| Approach: | They propose to use a common subword unit vocabulary and byte-level encoding to fine tune two subword-level models and one byte level model on hand-corrected error corpora. |
| Outcome: | The proposed model improves accuracy for spelling and grammatical errors and more complex errors. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
Copied to clipboard
| Challenge: | Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models. |
| Approach: | They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models. |
| Outcome: | The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines. |
Parallel Iterative Edit Models for Local Sequence Transduction (D19-1)
Copied to clipboard
| Challenge: | Recent approaches to local sequence transduction are based on the popular encoder-decoder model for sequence to sequence learning. |
| Approach: | They propose a parallel iterative edit model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). |
| Outcome: | The proposed model is faster and more accurate than the current encoder-decoder model for local sequence transduction tasks like translation and paraphrasing. |
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection (D18-1)
Copied to clipboard
| Challenge: | grammatical error correction is a labor-intensive task that requires large amounts of training data. |
| Approach: | They propose to use a human-annotated corpus of human-generated grammatical errors to generate a synthetic model. |
| Outcome: | The proposed method outperforms the current state of the art in grammatical error correction . human annotators achieve 39.39 F1 scores, suggesting the model generates mostly human-like instances . |
Improving Grammatical Error Correction with Multimodal Feature Integration (2023.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that multimodal GEC models improve over strong baselines and achieve a new state-of-the-art result on the Falko-MERLIN test set. |
| Approach: | They propose a framework that integrates both speech and text features to enhance GEC by generating audio from text using advanced text-to-speech models. |
| Outcome: | The proposed framework improves on CoNLL14, BEA19 English, and Falko-MERLIN German datasets. |
LM-Critic: Language Models for Unsupervised Grammatical Error Correction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work casts GEC as a translation problem using encoder-decoder models to map bad (ungrammatical) sentences into good (grammatically) sentences. |
| Approach: | They propose to use a pretrained language model to define an LM-Critic that judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. |
| Outcome: | The proposed method outperforms existing methods in both the unsupervised and supervised setting. |