ChenxinAn-fdu / CoNT

Fairseq Code for Neurips 2022 paper: "CoNT: Contrastive Neural Text Generation"

CoNT

Fairseq Code for Neurips 2022 paper: "CoNT: Contrastive Neural Text Generation"

This is the fairseq-based implementation for NeurIPS 2022 paper: CoNT: Contrastive Neural Text Generation. CoNT is a strong contrastive learning framework for neural text generation which outperforms the MLE based training method on five generation tasks, including machine translation, summarization, code comment generation, data-to-text generation, commensense generation. This repo is mainly about machine translation tasks. For other tasks please refer to our transformers repo

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Dependencies

Main libraries

• Python 3.7
• PyTorch 1.7 +
• fairseq 1.0.0

# clone our repo and fairseq
git clone https://github.com/ChenxinAn-fdu/CoNT.git
git clone https://github.com/facebookresearch/fairseq.git
# replace the fairseq folder with our custom code
rm -rf fairseq/fairseq && mv CoNT/fairseq  fairseq/
mv fairseq CoNT/ && cd CoNT/fairseq && pip install -e . && cd ..

Please follow the instruction in Fairseq to prepare the data.

We have provided the training scripts for IWSLT14 and WMT14 translation tasks which can make it very easy to reproduce our results: run_iwslt14.py and run_wmt14.py.

Generating binarized_dataset

python run_iwslt14.py --mode preprocess

Training

For example, if you have 4 V100-32 GPUs, run the following script for training with warmup:

python run_iwslt14.py --mode train --gpus 0,1,2,3 --warmup

If the --save-dir has already has a warmed-up checkpoint, you can directly omit the --warmup option

python run_iwslt14.py --mode train --gpus 0,1,2,3

Testing

python run_iwslt14.py --mode gen --gpus 0 --save_path /path/to/checkpoints/checkpoint_best.pt

With checkpoints average

python run_iwslt14.py --mode gen --gpus 0 --save_path /path/to/checkpoints/ --avg_ckpt

Tips of reproducing results on WMT14

• Compound splitting: Previous work usually reports the results with compound splitting on WMT'14 En-De translation task. To apply compound splitting to the reference file and output file, please run the following cmd:

python run_wmt14.py --mode score --out_file /path/to/checkpoints/checkpoint_best.out

• Training set: Fairseq has provided two versions of training set: (1) 3.9M training samples and (2) 4.5M training samples. We reported the results with the 4.5M training set (the same as the original paper transformer) in this paper. Using the 3.9M training set will lead to better results.

Citing

@article{an2022cont,
  title={CoNT: Contrastive Neural Text Generation},
  author={An, Chenxin and Feng, Jiangtao and Lv, Kai and Kong, Lingpeng and Qiu, Xipeng and Huang, Xuanjing},
  journal={arXiv preprint arXiv:2205.14690},
  year={2022}
}