An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction (D19-1)
Copied to clipboard
| Challenge: | incorporating pseudo data in the training of grammatical error correction models has been a key factor in improving performance of such models. |
| Approach: | They investigate the choice of how pseudo data should be generated or used in a grammatical error correction model and show that the results are state-of-the-art. |
| Outcome: | The proposed method achieves state-of-the-art on the CoNLL-2014 test set and the official test set of the BEA-2019 shared task without making any modifications to the model architecture. |
Similar Papers
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner’s Error Tendency (2020.acl-srw)
Copied to clipboard
| 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. |
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Various data augmentation strategies have been proposed to improve GEC models . high-quality parallel data for GEC is not as widely available . |
| Approach: | They propose a data augmentation approach that strategically augments real data by generating pseudo data. |
| Outcome: | The proposed approach significantly improves GEC models on English and Chinese datasets. |
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation (2020.coling-main)
Copied to clipboard
| 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. |
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection (D18-1)
Copied to clipboard
| Challenge: | grammatical error correction is a labor-intensive task that requires large amounts of training data. |
| Approach: | They propose to use a human-annotated corpus of human-generated grammatical errors to generate a synthetic model. |
| Outcome: | The proposed method outperforms the current state of the art in grammatical error correction . human annotators achieve 39.39 F1 scores, suggesting the model generates mostly human-like instances . |
Improving Grammatical Error Correction via Contextual Data Augmentation (2024.findings-acl)
Copied to clipboard
| Challenge: | Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data. |
| Approach: | They propose a method for augmentation of synthetic data with a more consistent error distribution. |
| Outcome: | The proposed method outperforms strong baselines and achieves state-of-the-art with only a few synthetic data. |
Do Grammatical Error Correction Models Realize Grammatical Generalization? (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models for grammatical error correction use pseudo data, but they are inconvenient for realworld deployment due to large amounts of training data. |
| Approach: | They propose a method to evaluate whether GEC models can generalize to unseen errors by using synthetic and real GEC datasets with controlled vocabularies. |
| Outcome: | The proposed model fails to realize grammatical generalization even in simple settings with limited vocabulary and syntax, suggesting it lacks the generalization ability required to correct errors from provided training examples. |
Comparison of Grammatical Error Correction Using Back-Translation Models (2021.naacl-srw)
Copied to clipboard
| Challenge: | Currently, a mainstream approach to generate pseudo data is back-translation (BT). |
| Approach: | They propose to use back-translation to generate pseudo data that contains grammatical and ungrammatically produced sentences. |
| Outcome: | The proposed methods improve or interpolate the performance of each error type compared with a single BT model with different seeds. |
Data Weighted Training Strategies for Grammatical Error Correction (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent advances in the task of Grammatical Error Correction (GEC) have been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. |
| Approach: | They propose to incorporate delta-log-perplexity, a type of example scoring, into a training schedule for Grammatical Error Correction (GEC) they perform experiments that shed light on the function and applicability of delta- log-perplicity. |
| Outcome: | The proposed methods incorporate delta-log-perplexity, a type of example scoring, into a training schedule for the task. |
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation (2020.lrec-1)
Copied to clipboard
| Challenge: | a recent study shows that typographical errors are now ubiquitous . traditional spelling correction software is inadequate to correct typographical mistakes . |
| Approach: | They propose to generate typographical errors based on annotated spelling errors . they then use annotations to introduce errors into substantially larger corpora . |
| Outcome: | The proposed method generates typographical errors that require context-aware error detection . it also shows that machine learning can correct typographical mistakes based on the data . |
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (P19-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors. |
| Approach: | They propose to use automatic grammatical error correction to improve seq2seq models . they conduct experiments on machine translation, formality style transfer, sentence compression and simplification . |
| Outcome: | The proposed system can improve grammaticality of generated text and improve formal style tasks. |