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Found 12 papers, 2 papers with code
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model
In this study, we explored the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC.
Cross-lingual Transfer Learning for Grammatical Error Correction
Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks.
Chinese Grammatical Correction Using BERT-based Pre-trained Model
In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization.
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner's Error Tendency
To address the limitations of language and computational resources, we assume that introducing pseudo errors into sentences similar to those written by the language learners is more efficient, rather than incorporating random pseudo errors into monolingual data.
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC.
Ranked #12 on
Grammatical Error Correction
on CoNLL-2014 Shared Task
Automated Essay Scoring System for Nonnative Japanese Learners
In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores.
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track
Thus, it is not straightforward to utilize language representations trained from a large corpus, such as Bidirectional Encoder Representations from Transformers (BERT), in a form suitable for the learner{'}s grammatical errors.
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation.
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation.
Controlling Grammatical Error Correction Using Word Edit Rate
In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction.
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
For Japanese-to-English translation, this method achieves a BLEU score that is 0. 56 points more than that of a baseline.
