Heterogeneous Recycle Generation for Chinese Grammatical Error Correction (2020.coling-main)
Copied to clipboard
| Challenge: | Recent work in the field of grammatical error correction (GEC) rely on neural machine translation-based models. |
| Approach: | They propose a heterogeneous approach to Chinese grammatical error correction using NMT-based models, sequence editing models, and a spell checker. |
| Outcome: | The proposed model achieves state-of-the-art performance without data augmentation or changes in architecture . it adapts the ERRANT scorer to be able to score Chinese sentences . |
Similar Papers
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation (N18-2)
Copied to clipboard
| Challenge: | Currently, most effective GEC systems are based on phrase-based statistical machine translation. |
| Approach: | They combine two of the most popular approaches to automated Grammatical Error Correction (GEC) they create a hybrid GEC system that preserves the accuracy of SMT output and generates more fluent sentences . |
| Outcome: | The proposed system achieves state-of-the-art on the CoNLL-2014 and JFLEG benchmarks. |
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | grammatical error correction (GEC) is a complex task that requires high-quality data from native speakers. |
| Approach: | They propose a human-annotated corpus to detect, identify and correct grammatical errors in Chinese examinations. |
| Outcome: | The proposed model outperforms other models in low-resource settings, but there is a significant gap between the models and humans that encourages future models to bridge it. |
Adversarial Grammatical Error Correction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines. |
| Approach: | They propose an adversarial approach to Grammatical Error Correction using a transformer-based model and a sentence-pair classification model. |
| Outcome: | The proposed approach achieves competitive GEC quality compared to baselines. |
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Chinese Grammatical Error Correction (CGEC) aims to generate correct sentences from erroneous sequences. |
| Approach: | They propose a zero-shot approach for spelling error correction that is simple but effective . they propose auxiliary task to predict POS sequence of target sentence . |
| Outcome: | The proposed framework achieves 42.11 F-0.5 on the English GEC dataset outperforms the previous state-of-the-art by a wide margin of 1.30 points. |
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. |
UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework (2025.coling-demos)
Copied to clipboard
| Challenge: | Existing tools for GEC have been developed to support research on grammatical errors, but there is no comprehensive evaluation on these models. |
| Approach: | They propose an open-source framework for Grammatical Error Correction that integrates 5 widely-used GEC models and compares their performance on 7 datasets in different languages. |
| Outcome: | The proposed framework compares 5 widely-used models on 7 datasets in different languages. |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
Copied to clipboard
| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
| Approach: | They propose a language-agnostic method to generate a large number of synthetic examples and use large-scale multilingual language models to train state-of-the-art GEC models. |
| Outcome: | The proposed method surpasses state-of-the-art results on GEC benchmarks in English, Czech, German and Russian. |
GPT-3.5 for Grammatical Error Correction (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent work shows that GPT-3.5 struggles with several error types, including punctuation mistakes, tense errors, syntactic dependencies between words, and lexical compatibility at the sentence level. |
| Approach: | They evaluate GPT-3.5 for grammatical error correction in multiple languages . they use it to re-rank correction hypotheses generated by other GEC models . |
| Outcome: | The proposed model performs well in English and Russian, but struggles with errors in other languages. |
Cross-Sentence Grammatical Error Correction (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to automatic grammatical error correction (GEC) ignore cross-sentence context . existing approaches only correct one sentence at a time and ignore useful contextual information . |
| Approach: | They propose to use an auxiliary encoder that encodes previous sentences and incorporates the encoding in the decoder via attention and gating mechanisms. |
| Outcome: | The proposed model improves over strong baselines on a synthetic dataset showing high performance in verb tense corrections that require cross-sentence context. |
A Crash Course in Automatic Grammatical Error Correction (2020.coling-tutorials)
Copied to clipboard
| Challenge: | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. |
| Approach: | tutorial aims to introduce participants to the field of Grammatical Error Correction . aim is to examine the development of neural-based GEC systems . |
| Outcome: | the tutorial aims to introduce participants to the current state of the art in the field of Grammatical Error Correction (GEC) |