The BEA-2019 Shared Task on Grammatical Error Correction

By Christopher Bryant, Mariano Felice, Øistein E. Andersen, Ted Briscoe. 2019

378 citations recorded by the source corpus.

This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write&Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F_0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “The dataset is split 23,652/2,042/2,045 into training/validation/test.”
Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding (2021, methodology): “…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 training data.”
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020, methodology): “We use the BEA-2019 workshop 1 (Bryant et al., 2019) ofﬁcial shared task data as training and development sets.”
Erroneous data generation for Grammatical Error Correction (2019, methodology): “Our ﬁne-tuning data is Lang-8 (Mizumoto et al., 2011; Tajiri et al., 2012), NUS Corpus of Learner English (NUCLE) (Dahlmeier et al., 2013), FCE (Yannakoudakis et al., 2011), the Cambridge English Write & Improve (W&I) corpus and the LOCNESS corpus (ABCN) (Granger, 1998; Bryant et al., 2019) .”
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models (2023, background): “These tasks include (Gao et al., 2020) and WikiHow (Koupaee and Wang, 2018)) or existing NLP tasks (BigBench (Srivastava et al., 2022), Enron (Cohen, 2015), BEA-2019 shared task on grammatical error correction (Bryant et al., 2019)).”
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data (2019, methodology): “, 2013), W&I+LOCNESS data sets (Bryant et al., 2019; Granger, 1998), and a preprocessed version of the Lang-8 Corpus of Learner English (Mizumoto et al.”
GECToR – Grammatical Error Correction: Tag, Not Rewrite (2020, background): “els have achieved state-of-the-art performance on standard GEC benchmarks (Bryant et al., 2019).”
Improving Grammatical Error Correction with Machine Translation Pairs (2019, methodology): “We conduct experiments on both the BEA 19 (Bryant et al., 2019) and the CoNLL 2014 (Ng et al., 2014) benchmark datasets to evaluate our approach.”
Seq2Edits: Sequence Transduction Using Span-level Edit Operations (2020, methodology): “5-scores on the BEA development and test sets (Bryant et al., 2019),”
A Comprehensive Survey of Grammatical Error Correction (2021, methodology): “However, for better application of GEC, prospective GEC systems should provide more individualized feedback to English learners, taking consideration of their L1s.”
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning (2019, methodology): “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.”
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020, positive): “…experiments in the same setting with the restricted track of the BEA-2019 GEC shared task (Bryant et al., 2019), using FCE (Yannakoudakis et al., 2011), Lang-8 Corpus of Learner English (Mizu-moto et al., 2011), NUCLE (Dahlmeier et al., 2013) and W&I+LOCNESS (Granger, 1998; Bryant et al., 2019)”
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation (2020, methodology): “, 2011), and Write & Improve + LOCNESS Corpus(W&I+LOCNESS)(Bryant et al., 2019).”
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020, background): “Transformer-big.”
Encode, Tag, Realize: High-Precision Text Editing (2019, methodology): “We use a recent benchmark from a shared task of the 2019 Building Educational Applications workshop, specifically from the Low Resource track7 (Bryant et al., 2019).”
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding (2022, methodology): “We follow recent work in English GEC to 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”