Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses (2020.emnlp-main)
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| Challenge: | CWEB is a new benchmark for grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Approach: | They propose to broaden the target domain of grammatical error correction (GEC) systems . website data contains far fewer grammamatical errors than learner essays . |
| Outcome: | The proposed model can't rely on a strong internal language model in low error density domains. |
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| Challenge: | grammatical error correction (GEC) is a text generation task . performance on low error density domains where texts written by native speakers can be improved. |
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| Challenge: | grammatical error correction (GEC) systems outperform humans on the CoNLL-2014 test set, but there are still classes of errors that they fail to correct. |
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| Challenge: | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. |
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| Challenge: | Existing models for grammatical error correction use pseudo data, but they are inconvenient for realworld deployment due to large amounts of training data. |
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How Good (really) are Grammatical Error Correction Systems? (2021.eacl-main)
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Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough? (N19-1)
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| Challenge: | Existing studies have evaluated grammatical error correction models on a single corpus, but the evaluation is incomplete because the task difficulty varies depending on the corpus and conditions such as proficiency levels of the writers and essay topics. |
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