Using Wikipedia Edits in Low Resource Grammatical Error Correction

By Adriane Boyd. 2018

78 citations recorded by the source corpus.

We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Boyd (2018) use German edits from Wikipedia revision history and use those relating to GEC as augmented training data.”
Evaluating the Morphosyntactic Well-formedness of Generated Texts (2021, methodology): “We use the Falko-MERLIN GEC corpus (Boyd, 2018) for German and the RULEC-GEC dataset (Rozovskaya and Roth, 2019) for Russian.”
A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction (2020, methodology): “Boyd (2018) augmented training data with edits extracted from Wikipedia revision history in German.”
A Comprehensive Survey of Grammatical Error Correction (2021, background): “• German: Boyd built two GEC corpora for German: Falko and MERLIN [8].”