Artificial error generation for translation-based grammatical error correction
By Mariano Felice. 2016
18 citations recorded by the source corpus.
Subfields: Natural Language Processing, Machine Translation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Marcin Junczys-Dowmunt
- Christopher D. Manning
- Dan Jurafsky
- Philipp Koehn
- Roman Grundkiewicz
- Kenneth Heafield
- Noah A. Smith
- Felix Stahlberg
In Machine Translation
- Marcin Junczys-Dowmunt
- Roman Grundkiewicz
- Kenneth Heafield
- Dan Jurafsky
- Stanley Xie
- Ziang Xie
- Andrew Y. Ng
- Guillaume Genthial
Citations in the corpus
- Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data (2019, methodology): “Although our unsupervised method for synthesising parallel data by means of an (inverted) spellchecker is novel, the idea of generating artificial errors has been explored in the literature before, as summarized by Felice (2016). Previously proposed methods usually require a errorannotated corpus as”
- Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction (2018, methodology): “Mismatches in the distribution of error types can often severely impact the performance of data synthesis techniques for grammar correction (Felice, 2016).”