Alter the style: A neural text style transfer model

In this project we want to build a system that can transfer a language style into another style while preserving the main content (e.g. given a factual event, we can rewrite it like a romantic story).

Our goal:

1. Transer the style (e.g. positive -> negative, factual -> romantic).

2. Preserve the content.

This task is non-trivial, because normally we do not have sufficient data that have aligned sentences with the same content but expressed in different styles. Therefore, we have to learn a model which can disentangle style attributes and content given only unaligned sentences.

Model Architecture:

Usage

Training

python train.py --config sample_config.json

Train a negative auto-encoder based on data data/yelp/sentiment.train.0, which takes the neutral content and negative attributes as inputs, and will output a negative comment.
You can also train a positive auto-encoder by simply modifying the "src": "data/yelp/sentiment.train.1", in sample_config.json.

Testing

python test.py --config sample_config.json --bleu

Generate a negative comment based on positive comment dataset data/yelp/sentiment.truth.1, the procedure is as follows:

• Take a positive comment, and extract its neutral content as the one of the inputs;

• Retrieve a negative comment based on the neutral content, and extract its negative attributes as one of the inputs;

• Combine the neutral content and negative attributes together, and feed them into the well-trained model;

• Output the generated negative comment.

The script will generate the following files:

• sample_run/inputs.epoch : the input neutral contents

• sample_run/auxs.epoch : the input negative attributes

• sample_run/preds.epoch : the generated negative comments

• sample_run/golds.epoch : the ground truth negative comments

And you can also see the precision, recall, edit_distance and rouge score on the logging info.

Reference

• Li, Juncen, et al. "Delete, retrieve, generate: A simple approach to sentiment and style transfer." arXiv preprint arXiv:1804.06437 (2018).

• https://github.com/lijuncen/Sentiment-and-Style-Transfer

• https://github.com/rpryzant/delete_retrieve_generate