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text-autoencoders

This repo contains the code and data of the following paper:
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Tianxiao Shen, Jonas Mueller, Regina Barzilay, and Tommi Jaakkola

We support plain autoencoder (AE), variational autoencoder (VAE), adversarial autoencoder (AAE), Latent-noising AAE (LAAE), and Denoising AAE (DAAE).

Once the model is trained, it can be used to generate sentences, map sentences to a continuous space, perform sentence analogy and interpolation.

Video

overview

Dependencies

The code has been tested in Python 3.7, PyTorch 1.1

Download data

Download the processed Yelp and Yahoo datasets by running:

bash download_data.sh

Training

The basic training command is:

python train.py --train data/yelp/train.txt --valid data/yelp/valid.txt --model_type aae --lambda_adv 10 --noise 0.3,0,0,0 --save-dir checkpoints/yelp/daae

To train various models, use the following options:

  • AE: --model_type dae --save-dir checkpoints/yelp/ae
  • VAE: --model_type vae --lambda_kl 0.1 --save-dir checkpoints/yelp/vae_kl0.1
  • AAE: --model_type aae --lambda_adv 10 --save-dir checkpoints/yelp/aae
  • LAAE: --model_type aae --lambda_adv 10 --lambda_p 0.01 --save-dir checkpoints/yelp/aae_p0.01
  • DAAE: --model_type aae --lambda_adv 10 --noise 0.3,0,0,0 --save-dir checkpoints/yelp/daae, where --noise P,P,P,K specifies word drop probability, word blank probability, word substitute probability, max word shuffle distance, respectively

Run python train.py -h to see all training options.

Testing

After training, the model can be used for different tasks.

To reconstruct input data, run:

python test.py --reconstruct --data data/yelp/test.txt --output test.rec --checkpoint checkpoints/yelp/daae/

To generate sentences from the model, run:

python test.py --sample --n 10000 --output sample --checkpoint checkpoints/yelp/daae/

To perform sentence manipulation via vector arithmetic, run:

python test.py --arithmetic --data data/yelp/tense/valid.past,data/yelp/tense/valid.present,data/yelp/tense/test.past --output test.past2present --checkpoint checkpoints/yelp/daae/
python test.py --arithmetic --k 2 --data data/yelp/sentiment/100.neg,data/yelp/sentiment/100.pos,data/yelp/sentiment/1000.neg --output 1000.neg2pos --checkpoint checkpoints/yelp/daae/

where the difference between the average latent representation of the first two data files will be applied to the third file (separated by commas), and k denotes the scaling factor.

To perform sentence interpolation between two data files (separated by a comma), run:

python test.py --interpolate --data data/yelp/interpolate/example.long,data/yelp/interpolate/example.short --output example.int --checkpoint checkpoints/yelp/daae/

The output file will be stored in the checkpoint directory.