A New Dataset and Method for Automatically Grading ESOL Texts

By Helen Yannakoudakis, Ted Briscoe, Ben Medlock. 2011

650 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

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.”
Linguistic Knowledge and Transferability of Contextual Representations (2019, methodology): “We study six publicly-available models for contextualized word representation in English.”
Exploring and Predicting Transferability across NLP Tasks (2020, methodology): “, 1993) 38K GED (Yannakoudakis et al., 2011) 29K NER (Tjong Kim Sang and De Meulder, 2003) 14K POS-EWT (Silveira et al.”
Semi-supervised Multitask Learning for Sequence Labeling (2017, methodology): “As the ﬁrst benchmark, we use the publicly released First Certiﬁcate in English (FCE, Yannakoudakis et al. (2011)) dataset, containing 33,673 manually annotated sentences.”
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020, positive): “, 2019), using FCE (Yannakoudakis et al., 2011), Lang-8 Corpus of Learner English (Mizu-”
Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE (2015, methodology): “Williams test has additionally been used in evaluation of systems that automatically assess spoken and written language quality (Yannakoudakis et al., 2011; Yannakoudakis and Briscoe, 2012; Evanini et al., 2013).”
Seq2Edits: Sequence Transduction Using Span-level Edit Operations (2020, methodology): “…correction we follow a multistage fine-tuning setup.8 After training on the common pre-training data described above, we finetune for 30K steps on the public Lang-8 (Mizumoto et al., 2012) corpus, followed by 500 steps on the FCE (Yannakoudakis et al., 2011) and W&I (Bryant et al., 2019) corpora.”
An automated essay scoring systems: a systematic literature review (2021, background): “And Zupanc and Bosnic (2014) also used the word coherence to evaluate essays.”
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction (2020, methodology): “The To provide an additional benchmark for evaluation, we also evaluate on the test dataset from the FCE corpus (Yannakoudakis et al., 2011).”
The BEA-2019 Shared Task on Grammatical Error Correction (2019, background): “FCE The First Certificate in English (FCE) corpus is a subset of the Cambridge Learner Corpus (CLC) that contains 1,244 written answers to FCE exam questions (Yannakoudakis et al., 2011).”
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020, methodology): “To train BERT for BERT-fuse mask and GED, we use W&I-train, NUCLE, and FCE-train as training, and W&I-dev was used as development data.”
Erroneous data generation for Grammatical Error Correction (2019, methodology): “Since the NUCLE, FCE and ABCN datasets are much smaller than the Lang-8 set, we did not ﬁlter out the error-free sentence pairs in these datasets.”
GECToR – Grammatical Error Correction: Tag, Not Rewrite (2020, background): “…Corpus of Learner English (NU-CLE) 4 (Dahlmeier et al., 2013), Lang-8 Corpus of Learner English (Lang-8) 5 (Tajiri et al., 2012), FCE dataset 6 (Yannakoudakis et al., 2011), the publicly available part of the Cambridge Learner Corpus (Nicholls, 2003) and Write & Improve + LOC-NESS Corpus…”
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation (2020, methodology): “…as original training corpus: National University of Singapore Corpus of Learner English (NUCLE)(Dahlmeier et al., 2013), Lang-8 Corpus of Learner English (Lang-8)(Tajiri et al., 2012), FCE dataset(Yannakoudakis et al., 2011), and Write & Improve + LOCNESS Corpus(W&I+LOCNESS)(Bryant et al., 2019).”
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data (2019, methodology): “256 FCE corpus (Yannakoudakis et al., 2011), NUCLE (Dahlmeier et al.”
Automatic Text Scoring Using Neural Networks (2016, methodology): “As described above, the SVM model has rich linguistic knowledge and consists of hand-picked features which have achieved excellent performance in similar tasks (Yannakoudakis et al., 2011).”
A Comprehensive Survey of Grammar Error Correction (2020, methodology): “We first introduce several datasets that are most widely used to develop GEC systems based on supervised approach, including NUCLE [3], Lang-8 [4], FCE [5], J FLEG [6], Write&Improve+LOCNESS [7].”
The CoNLL-2013 Shared Task on Grammatical Error Correction (2013, methodology): “For example, participating teams are free to use the Cambridge FCE corpus (Yannakoudakis et al., 2011; Nicholls, 2003) Error annotation on the test essays was carried out by a native speaker of English who is a lecturer at the NUS Centre for English Language Communication.”
A Report on the First Native Language Identification Shared Task (2013, background): “…used in the competition included the: • ICLE : which covered all L1s except for Arabic, Hindi and Telugu; • FCE: First Certiﬁcate in English Corpus (Yannakoudakis et al., 2011): a collection of essay written for an English assessment exam, which covered all L1s except for Arabic, Hindi and…”