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Type: Organization
Bio: Automatic grammatical error correction
Type: Organization
Bio: Automatic grammatical error correction
Baseline models, training scripts, and instructions on how to reproduce our results for our state-of-art grammar correction system from M. Junczys-Dowmunt, R. Grundkiewicz: Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction, EMNLP 2016.
This repository contains materials for our tutorial on automatic grammatical error correction: R. Grundkiewicz, C. Bryant, M. Felice: A Crash Course in Automatic Grammatical Error Correction, COLING 2020.
Data and scripts for our EMNLP 2015 paper: Grundkiewicz, Roman and Junczys-Dowmunt, Marcin and Gillian, Edward. Human Evaluation of Grammatical Error Correction Systems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.
Webpage on Automatic Grammatical Error Correction, http://grammatical.github.io
Models and training scripts for the English, German and Russian MAGEC systems described in R. Grundkiewicz, M. Junczys-Dowmunt: Minimally-Augmented Grammatical Error Correction, W-NUT 2019.
Neural models and instructions on how to reproduce our results for our neural grammatical error correction systems from M. Junczys-Dowmunt, R. Grundkiewicz, S. Guha, K. Heafield: Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task, NAACL 2018.
Models, system configurations and outputs of our winning GEC systems in the BEA 2019 shared task described in R. Grundkiewicz, M. Junczys-Dowmunt, K. Heafield: Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data, BEA 2019.
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