Papers with GEC model
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. |
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. |
Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)
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| Challenge: | Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available. |
| Approach: | They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities . |
| Outcome: | The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text. |
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. |
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020.acl-main)
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| Challenge: | Existing methods for incorporating a masked language model into an EncDec model have potential drawbacks when applied to GEC. |
| Approach: | They propose to incorporate a pre-trained masked language model (MLM) into an encoder-decoder model for grammatical error correction. |
| Outcome: | The proposed method achieves state-of-the-art on BEA-2019 and CoNLL-2014 benchmarks. |
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains (2021.findings-emnlp)
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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. |
| Approach: | They propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate. |
| Outcome: | The proposed approach significantly improves the performance of GEC models in low error density domains. |