Papers with GEC task
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner’s Error Tendency (2020.acl-srw)
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| Challenge: | Recent studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data. |
| Approach: | They propose to extract sentences similar to those written by language learners and generate pseudo errors by considering error types that learners often make. |
| Outcome: | The proposed model significantly improves the performance of the Russian GEC task compared with other models using pseudo data. |
Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models (2024.findings-emnlp)
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| 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. |
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
Cool English: a Grammatical Error Correction System Based on Large Learner Corpora (C18-2)
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| Challenge: | Existing systems that correct grammatical errors are lacking in second language learning due to limited vocabulary and inadequate command of grammar. |
| Approach: | They propose a grammatical error correction system that provides corrective feedback for essays using a sequence-to-sequence model. |
| Outcome: | The proposed system achieves competitive performance on a number of publicly available testsets. |
Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing methods of acquiring diverse outputs focus on revising all tokens of a sentence. |
| Approach: | They propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap. |
| Outcome: | The proposed method generates more diverse corrections without losing accuracy in the local sequence transduction task. |
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation (2020.coling-main)
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| Challenge: | Existing methods for enhancing grammatical error correction use noise to generate tokens . existing methods only generate sentences with limited error types, which leads to lack of diversity of generated errors. |
| Approach: | They propose a data augmentation method that can apply noise to latent representations of a sentence to generate synthetic samples with various error types. |
| Outcome: | The proposed method improves performance and robustness of existing models on public benchmarks and on FCE benchmarks. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
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| 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. |
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (2023.emnlp-main)
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| Challenge: | Evaluating the performance of Grammatical Error Correction systems is a challenging task due to its subjectivity. |
| Approach: | They propose a method to evaluate GEC systems in multi-reference evaluation setting . they use consistent edit boundaries to eliminate bias caused by inconsistent edit boundaries . |
| Outcome: | The proposed evaluation metric eliminates bias caused by inconsistent edit boundaries on six English reference sets. |
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)
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| Challenge: | Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit. |
| Approach: | They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets. |
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
| Approach: | They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction. |
| Outcome: | The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics. |
LM-Critic: Language Models for Unsupervised Grammatical Error Correction (2021.emnlp-main)
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| 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. |