| 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. |
Similar Papers
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation (N18-2)
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| 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. |
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)
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| Challenge: | Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models. |
| Approach: | They propose a syntax-enhanced grammatical error correction approach called SynGEC that incorporates dependency syntactic information into the encoder part of GEC models. |
| Outcome: | The proposed approach outperforms strong baselines and achieves competitive performance on mainstream English and Chinese GEC datasets. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
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| 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. |
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule (2023.emnlp-main)
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| Challenge: | Recent research has focused on using synthetic data for grammatical error correction . lack of annotated training data hinders progress in the field . |
| Approach: | They propose auxiliary tasks that exploit alignment between original and corrected sentences . they propose a sequence-to-sequence problem and perform multi-task training . |
| Outcome: | The proposed auxiliary tasks outperform the best models with a BART-based model on 11B parameters. |
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)
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| Challenge: | Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems. |
| Approach: | They propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. |
| Outcome: | The proposed methods outperform state-of-the-art neural GEC systems by 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. |
Adversarial Grammatical Error Correction (2020.findings-emnlp)
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| 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. |
Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction (2025.findings-acl)
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| Challenge: | a novel method for encoding fine-grained error patterns improves performance on GEC. |
| Approach: | They propose a method for encoding grammatical errors from LLMs' internal states using a GER method. |
| Outcome: | The proposed method significantly boosts performance in ICL settings on multilingual GEC datasets. |
Neural Quality Estimation of Grammatical Error Correction (D18-1)
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| 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. |
Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models (2023.findings-acl)
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| Challenge: | Autoregressive models assign low probabilities to tokens that need corrections . grammatical error correction (GEC) is widely applied to natural language processing tasks . |
| Approach: | They propose to use a non-autoregressive model as an auxiliary model to train GEC models to correct grammatical errors in sentences. |
| Outcome: | The proposed method outperforms baselines on English and Chinese GEC tasks significantly. |
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |