Discover grammatical/neural-naacl2018 Open Source project

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Python
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MIT License
Created over 6 years ago
Updated over 5 years ago

grammatical

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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.

baselines-emnlp2016

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.

Perl

pretraining-bea2019

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.

Shell

coling2020-tutorial

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.

TeX

magec-wnut2019

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.

Python