Adversarial Decomposition of Text Representation

The code for the paper "Adversarial Decomposition of Text Representation", NAACL 2019 https://arxiv.org/abs/1808.09042

Installation

1. Clone this repo: https://github.com/itachicom615/keigo_adversarial_decomposition.git
2. Install PyTorch v1.1.0: pip install https://download.pytorch.org/whl/cu100/torch-1.1.0-cp37-cp37m-linux_x86_64.whl (for python3.6, use pip install https://download.pytorch.org/whl/cu100/torch-1.1.0-cp36-cp36m-linux_x86_64.whl)
3. Install dependencies: pip install -r requirements.txt
4. Download spacy models: python -m spacy download ja_core_news_lg==3.2.0
5. Download gensim: pip install gensim==3.8.3

Initial setup

1. Create dir mkdir -p data/experiments
2. Create dir mkdir -p data/datasets
3. Create dir mkdir -p data/word_embeddings
4. Download the Keigo data: git clone https://github.com/itachicom615/keigo_extraction.git data/datasets/keigo
5. Download the pickled fastText embeddings wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ja.300.vec.gz data/word_embeddings

Running the code

Global constants are set in the file settings.py. In general, you don't need to change this file. Experiment parameters are set in the config.py file.

First, run the preprocessing script: python preprocess_train.py

This scirpt will print the ID of the training experiment. You can paste it in the eval_generation.ipynb notebook to play with the model.

Chaning the form and meaning

The provided eval_generation.ipynb notebook shows how to use the model to swap the meaning and form vectors of the input sentences!

Citation

If you find this code helpful, please consider citing our paper:

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