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Self-Training for Neural Sequence Generation

This repo includes instructions for running noisy self-training algorithms from the following paper:

Revisiting Self-Training for Neural Sequence Generation
Junxian He*, Jiatao Gu*, Jiajun Shen, Marc'Aurelio Ranzato
ICLR 2020

Requirement

  • fairseq (please see the fairseq repo for other requirements on Python and PyTorch versions)

fairseq can be installed with:

pip install fairseq

Data

Download and preprocess the WMT'14 En-De dataset:

# Download and prepare the data
wget https://raw.githubusercontent.com/pytorch/fairseq/master/examples/translation/prepare-wmt14en2de.sh
bash prepare-wmt14en2de.sh --icml17

TEXT=wmt14_en_de
fairseq-preprocess --source-lang en --target-lang de \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir wmt14_en_de_bin --thresholdtgt 0 --thresholdsrc 0 \
    --joined-dictionary --workers 16

Then we mimic a semi-supervised setting where 100K training samples are randomly selected as parallel corpus and the remaining English training samples are treated as unannotated monolingual corpus:

bash extract_wmt100k.sh

Preprocess WMT100K:

bash preprocess.sh 100ken 100kde 

Add noise to the monolingual corpus for later usage:

TEXT=wmt14_en_de
python paraphrase/paraphrase.py \
  --paraphraze-fn noise_bpe \
  --word-dropout 0.2 \
  --word-blank 0.2 \
  --word-shuffle 3 \
  --data-file ${TEXT}/train.mono_en \
  --output ${TEXT}/train.mono_en_noise \
  --bpe-type subword

Train the base supervised model

Train the translation model with 30K updates:

bash supervised_train.sh 100ken 100kde 30000

Self-training as pseudo-training + fine-tuning

Translate the monolingual data to train.[suffix] to form a pseudo parallel dataset:

bash translate.sh [model_path] [suffix]  

Suppose the pseduo target language suffix is mono_de_iter1 (by default), preprocess:

bash preprocess.sh mono_en_noise mono_de_iter1

Pseudo-training + fine-tuning:

bash self_train.sh mono_en_noise mono_de_iter1 

The above command trains the model on the pseduo parallel corpus formed by train.mono_en_noise and train.mono_de_iter1 and then fine-tune it on real parallel data.

This self-training process can be repeated for multiple iterations to improve performance.

Reference

@inproceedings{He2020Revisiting,
title={Revisiting Self-Training for Neural Sequence Generation},
author={Junxian He and Jiatao Gu and Jiajun Shen and Marc'Aurelio Ranzato},
booktitle={Proceedings of ICLR},
year={2020},
url={https://openreview.net/forum?id=SJgdnAVKDH}
}

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Implementation of ICLR 2020 paper "Revisiting Self-Training for Neural Sequence Generation"

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