GenERRate: Generating Errors for Use in Grammatical Error Detection

By Jennifer Foster, Øistein E. Andersen. 2009

92 citations recorded by the source corpus.

This paper explores the issue of automatically generated ungrammatical data and its use in error detection, with a focus on the task of classifying a sentence as grammatical or ungrammatical. We present an error generation tool called GenERRate and show how GenERRate can be used to improve the performance of a classifier on learner data. We describe initial attempts to replicate Cambridge Learner Corpus errors using GenERRate.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Improving Grammatical Error Correction with Machine Translation Pairs (2019, methodology): “rupt monolingual corpora with either pre-defined rules or errors extracted from the seed parallel GEC data (Foster and Andersen, 2009; Zhao et al., 2019; Wang et al., 2019).”
Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data (2019, methodology): “Previously proposed methods usually require a error-annotated corpus as a seed to generate artiﬁcial errors reﬂecting linguistic properties and error distributions observed in natural-error corpora (Foster and Andersen, 2009; Felice and Yuan, 2014).”
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting (2019, background): “(Brockett et al., 2006; Foster and Andersen, 2009; Rozovskaya and Roth, 2010, 2011; Rozovskaya et al., 2012; Felice et al., 2014; Yuan et al., 2016; Rei et al., 2017; Xie et al., 2018), which studies generating source sentences with grammatical errors.”
Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study (2018, background): “…data augmentation along with training for GEC, despite some related studies that explore artificial error generation for GEC (Brockett et al., 2006; Foster & Andersen, 2009; Rozovskaya & Roth, 2010; 2011; Rozovskaya et al., 2012; Felice & Yuan, 2014; Xie et al., 2016; Rei et al., 2017; Xie et al.,…”
A Comprehensive Survey of Grammatical Error Correction (2021, methodology): “GenRRate [44] is an error generation tool with more consideration of POS, morphology, and context.”
Using Wikipedia Edits in Low Resource Grammatical Error Correction (2018, methodology): “In addition to using gold GEC corpora, which are typically fairly small in the context of MTbased approaches, research in GEC has taken a number of alternate data sources into consideration such as artificially generated errors (e.g., Wagner et al., 2007; Foster and Andersen, 2009; Yuan and Felice, ”
A Comprehensive Survey of Grammar Error Correction (2020, methodology): “GenRRate [113] is an error generation tool with more consideration of POS, morphology and context.”
Artificial error generation for translation-based grammatical error correction (2016, methodology): “Artificial errors have been shown to improve error correction performance, especially when used in conjunction with genuine error-annotated learner data (Foster and Andersen, 2009).”
Fluency Boost Learning and Inference for Neural Grammatical Error Correction (2018, methodology): “…data augmentation along with training for GEC, despite some previous studies that explore artiﬁcial error generation for GEC (Brockett et al., 2006; Foster and Andersen, 2009; Rozovskaya and Roth, 2010, 2011; Rozovskaya et al., 2012; Felice and Yuan, 2014; Xie et al., 2016; Rei et al., 2017).”
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2021, background): “Training with negative samples has been investigated in several classic NLP tasks, such as grammatical error detection (Foster and Andersen, 2009) and dialogue systems (Li et al., 2019).”