Mining Revision Log of Language Learning SNS for Automated Japanese Error Correction of Second Language Learners

By Tomoya Mizumoto, Mamoru Komachi, Masaaki Nagata, Yuji Matsumoto. 2011

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In Natural Language Processing

Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Mizumoto et al. (2011) DA by extracting Japanese GEC training data from the revision log of a language learning SNS. Hassan et al. (2017) Present a DA method to project words from closely-related high-resource languages to low-resource languages using word embedding representations.”
Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding (2021, methodology): “…conduct experiments in the restricted training setting of BEA-2019 GEC shared task (Bryant et al., 2019): We use Lang-8 Corpus of Learner English (Mizumoto et al., 2011), NUCLE (Dahlmeier et al., 2013), FCE (Yannakoudakis et al., 2011) and W&I+LOCNESS (Granger; Bryant et al., 2019) as our GEC…”
Controllable data synthesis method for grammatical error correction (2019, methodology): “JunczysDowmunt et al. (Junczys-Dowmunt and Grundkiewicz, 2014) use large-scale Lang-8 corpus to tune the Moses system (Koehn et al., 2007).”
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction (2018, methodology): “Datasets For training our sequence transduction models, we combine the publicly available English Lang-8 dataset, a parallel corpus collected from a language learner forum, with training data from the CoNLL 2014 challenge (Mizumoto et al., 2011; Ng et al., 2014).”
Overview of the NLPCC 2018 Shared Task: Grammatical Error Correction (2018, methodology): “Following [3], we collect a large-scale Chinese Mandarin learners’ corpus by exploring “language exchange” social networking services (SNS).”
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction (2020, methodology): “Mizumoto et al. (2011) extracted a Japanese learners corpus from the revision log of Lang-8 (about 1 million sentences) and implemented a character-based machine-translation model.”
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting (2019, methodology): “We follow the restricted setting where only the public resources can be used and use public Lang-8 (Mizumoto et al., 2011; Tajiri et al., 2012) and NUCLE (Dahlmeier et al., 2013) dataset as gold parallel data, as most previous work did.”
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020, methodology): “Speciﬁcally, to train a GEC model, we use W&I-train (Granger, 1998; Yannakoudakis et al., 2018), NUCLE (Dahlmeier et al., 2013), FCE-train (Yannakoudakis et al., 2011) and Lang-8 (Mizumoto et al., 2011) datasets.”
Parallel Iterative Edit Models for Local Sequence Transduction (2019, methodology): “We use Lang-8 (Mizumoto et al., 2011), NU-CLE (Ng et al., 2014) and FCE (Yannakoudakis et al., 2011) corpora, which jointly comprise 1.2 million sentence pairs in English.”
Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study (2018, methodology): “As previous studies (Ji et al., 2017), we use the public Lang-8 Corpus (Mizumoto et al., 2011; Tajiri et al., 2012), Cambridge Learner Corpus (CLC) (Nicholls, 2003) and NUS Corpus of Learner English (NUCLE) (Dahlmeier et al., 2013) as our original error-corrected training data.”
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding (2022, methodology): “, 2019): We use Lang-8 Corpus of Learner English (Mizumoto et al., 2011), NUCLE (Dahlmeier et al.”
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning (2019, methodology): “Our default setup is the “Restricted Track” scenario (§7.5) for the BEA 2019 Shared Task, where we use four data sources: the FCE dataset (Bryant et al., 2019), the Lang-8 dataset5 (Mizumoto et al., 2011; Tajiri et al., 2012), the NUCLE (v3.”
Improving Grammatical Error Correction with Machine Translation Pairs (2019, methodology): “We use the public available Lang8 (Mizumoto et al., 2011) and NUCLE (Dahlmeier et al., 2013) for fine-tuning the pretrained models.”
Fluency Boost Learning and Inference for Neural Grammatical Error Correction (2018, methodology): “As previous studies (Ji et al., 2017), we use the public Lang-8 Corpus (Mizumoto et al., 2011; Tajiri et al., 2012), Cambridge Learner Corpus (CLC) (Nicholls, 2003) and NUS Corpus of Learner English (NUCLE) (Dahlmeier et al., 2013) as our original error-corrected training data.”