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Repository of "An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction" (EMNLP-IJCNLP 2019)

Home Page: https://www.aclweb.org/anthology/D19-1119/

License: MIT License

Python 100.00%
grammatical-error-correction nlp-machine-learning subwords gec encoder-decoder

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gec-pseudodata's Issues

I can not reproduce the performance on CoNLL-2014

Hello, thanks for the efforts in GEC. I download the files and run it as the instruction. However, I get 61.94 F0.5 on CoNLL-2014.
CORRECT EDITS : 1211
PROPOSED EDITS : 1779
GOLD EDITS : 2659
P = 0.68071950534
R = 0.455434373825
F_0.5 = 0.619437340153
Precision : 0.6807
Recall : 0.4554
F_0.5 : 0.6194

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