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A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (IJCAI 2019)

<p align="center"><img src="fig/poster.png"></p>

Reproducibility

In order to help you quickly reproduce the existing works of text style transfer, we release the outputs of all models and the corresponding references.

Ps: We welcome other researchers pull request the outputs of your models.

Dataset

yelp: negative sentiment (0) <--> positive sentiment (1)

GYAFC: informal text (0) <--> formal text (1)

Since the GYAFC dataset is only free of charge for research purposes, we only publish a subset of the test dataset in the family and relationships domain (data/GYAFC/), the outputs (outputs/GYAFC/) of each system (including our model and all baselines) and the corresponding human references (references/GYAFC/). If you want to download the train and validation dataset, please follow the guidance at https://github.com/raosudha89/GYAFC-corpus. And then, name the corpora of two styles as the yelp dataset.

Quick Start

First of all, you should specify the dataset. For example, for yelp dataset:

export DATASET=yelp

If you want to use your own datasets, please follow the guidance of next section Extend to other tasks and datasets.

Step 1: Pre-train classifier

cd classifier
python textcnn.py --mode train

Note: If you get the error no module named opennmt, please install OpenNMT-tf: pip install OpenNMT-tf==1.15.0.

Step 2: Pre-train two seq2seq (nmt) models using pseudo-parallel data

2.1 Prepare pseudo-parallel data

To generate pseudo-parallel data, we follow the template-based method proposed by Li et al., 2018. And we have provided the pseudo-parallel data of the yelp dataset in the data/yelp/tsf_template directory. However, if you want to generate the pseudo-parallel data using templates, you can follow this link or design your own templates which are suitable for your task and dataset.

2.2 Pre-train two seq2seq (nmt) models

The default encoder and decoder are bilstm.

cd nmt
python nmt.py --mode train --nmt_direction 0-1 --n_epoch 5  # Pre-train forward (f) model
python nmt.py --mode train --nmt_direction 1-0 --n_epoch 5  # Pre-train backward (g) model

If you want to adopt transformer as encoder and decoder, run the following code:

cd nmt
python nmt.py --mode train --nmt_direction 0-1 --n_epoch 5 --n_layer 6 --encoder_decoder_type transformer
python nmt.py --mode train --nmt_direction 1-0 --n_epoch 5 --n_layer 6 --encoder_decoder_type transformer

Step 3: Dual reinforcement learning

python dual_training.py --n_epoch 10

The final transffered results are in the ../tmp/output/${DATASET}_final/ dir.

Extend to other tasks and datasets

If you don't have parallel or paired data, here are the processes you might go through:

  1. Prepare two unaligned (unpaired) corpora, one sentence per line
  2. Divide the dataset into train/dev/test
  3. Prepare pseudo-parallel corpus, you can use Li's method, or your own designed heuristic rules/templates
  4. Run step 1-3 in the section of Quick Start and specify the path to your new dataset or rename them like files in data/yelp/ and references/yelp/.

If you have parallel or paired data, here are the processes you might go through:

  1. Prepare two parallel corpora, one sentence per line
  2. Divide the dataset into train/dev/test
  3. Copy the dataset generated in the second step as the "pseudo-parallel" corpus to data/yelp/tsf_template
  4. Run step 1-3 in the section of Quick Start and specify the path to your new dataset or rename them like files in data/yelp/ and references/yelp/.

You can run the following code to see which parameters need to be set

python [dual_training.py | nmt.py | textcnn.py] --help

FAQ

About pseudo-parallel data

For some tasks, Li's method can't be used to generate pseudo-parallel data. Here are some related frequently asked questions:

  1. Can I use the original sentence to pre-train the two seq2seq models?

You can refer to this issue to generate pseudo-parallel data via simply add some noise to the original sentence.

  1. Can I use other style transfer models to generate pseudo-parallel data?

Of course, you can! Actually, we have tried to use CrossAlignment(Shen et al.,) to generated pseudo-parallel data. However, the experimental results are worse than using template-based methods.

  1. How about using several different methods to construct pseudo-parallel data?

We have tried to merge pseudo-parallel data generated by CrossAlignment (Shen et al.,) and Template-based(Li et al.,) to pre-train our model. There is a slight improvement in the experimental results.

  1. Can I train the model without pre-training with pseudo-parallel data?

This is an interesting question. I will try to remove the pre-training step. I think a feasible solution is to just initialize the word-embeddings of seq2seq (nmt) model, inspired by the three principles of unsupervised machine translation.

Tips

Note: No matter what method you use to construct pseudo-parallel data, the style transferred sentence or generated sentence y' (lower quality) should be the input, not the output (ground truth). This is validated to be important by our experiments. And what you need to actually do is to put y'\tx\n into files of tsf-template dir.

Dependencies

python==2.7
numpy==1.14.2
tensorflow==1.13.1
OpenNMT-tf==1.15.0

Cite

If you use this code, please cite the following paper:

@inproceedings{Luo19DualRL,
  author    = {Fuli Luo and
               Peng Li and
               Jie Zhou and
               Pengcheng Yang and
               Baobao Chang and
               Zhifang Sui and
               Xu Sun},
  title     = {A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer},
  booktitle = {Proceedings of the 28th International Joint Conference on Artificial Intelligence, {IJCAI} 2019},
  year      = {2019},            
}