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. |
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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. |
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. |
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. |
Grammatical Error Correction in Low-Resource Scenarios (D19-55)
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| 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. |
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. |
A Crash Course in Automatic Grammatical Error Correction (2020.coling-tutorials)
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| 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) |
Cross-lingual Transfer Learning for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing studies on English GEC have focused on improving it, but the resources required to train the models are not sufficient. |
| Approach: | They investigate cross-lingual transfer learning in grammatical error correction tasks . similarities between these languages is a key factor for successfully transferring grammatikal knowledge . |
| Outcome: | The proposed methods improve accuracy of grammatical error correction tasks in English and Russian, but lack the resources to train models in these languages. |
Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1 (D19-55)
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| Challenge: | Grammar error correction systems have become ubiquitous in a variety of software applications, but little is known about how to efficiently personalize them to the user’s characteristics, such as proficiency level and first language. |
| Approach: | They propose to adapt a general purpose neural GEC system to the proficiency level and the first language of a writer, using only a few thousand annotated sentences. |
| Outcome: | The proposed system improves on adapting to proficiency level and first language . the results are the broadest of its kind, covering five proficiency levels and twelve different languages. |
Controlling Grammatical Error Correction Using Word Edit Rate (P19-2)
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| Challenge: | Existing models for grammatical error correction only consider the single degree of correction suited for training corpus. |
| Approach: | They propose a neural grammar error correction method that can control the degree of correction by using new training data annotated with word edit rate. |
| Outcome: | The proposed method improves correction accuracy by using training data annotated with word edit rate. |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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| 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. |