PyTorch implementation of the models described in the AAAI2020 long paper Modeling Fluency and Faithfulness for Diverse Neural Machine Translation.
Implemented based on Fairseq-py, an open-source toolkit released by Facebook which was implemented strictly referring to Vaswani et al. (2017).
Download the preprocessed WMT'16 EN-DE data provided by Google and extract it and preprocess the dataset with a joined dictionary.
Train a base model.
$ sh run_ende.sh
Inference the test set.
$ python generate.py $DATA --path $SMODEL \
--gen-subset test --beam 4 --batch-size 128 \
--remove-bpe --lenpen 0.6 > pred.de \
# because fairseq's output is unordered, we need to recover its order
$ grep ^H pred.de | cut -f1,3- | cut -c3- | sort -k1n | cut -f2- > pred.de
If you find the resources in this repository useful, please consider citing:
@article{feng2019modeling,
title={Modeling Fluency and Faithfulness for Diverse Neural Machine Translation},
author={Feng, Yang and Xie, Wanying and Gu, Shuhao and Shao, Chenze and Zhang, Wen and Yang, Zhengxin and Yu, Dong},
journal={arXiv preprint arXiv:1912.00178},
year={2019}
}
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