Style Transfer from Non-Parallel Text by Cross-Alignment

Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi JaakkolaNIPS 2017

STYLE TRANSFER ON TEXT

• brief/verbose

• colloquial/professional

• personal/impersonal

• polite/impolite

NON-PARALLEL DATA

• Parallel :

• corresponding output for each input

• Usually not available

STYLE TRANSFER ON IMAGES

• Has had a lot of success

• Cycle GANs and other models…

• Not applicable to text

• because of discreteness of natural language

PROPOSED MODEL

X is generated from p(x|y, z)

**Important assumption: two datasets have to have the same content.

PROPOSED MODEL

• proposition 1: In this generative framework x1 and x2’s joint distribution can be recovered from their marginals only if for any different y1 and y2, distribution p(x|y1) and p(x|y2) are different.

• If the distribution of z has a more complex distribution, such as Gaussian mixture, then affine transformations can be uniquely determined.

METHOD (ELEMENTARY)

• Encoder-Decoder

• Reconstruction loss

• Variational Auto Encoder (VAE)

• imposes prior density p(z), z ~ N(0, I)

• KL-divergence regularizer to align posteriors

METHOD(ALIGNED AUTO-ENCODER)

• Relax the prior assumption on p(z)

• Use Lagrangian relaxation

• Adversarial loss

• Final loss:

METHOD(CROSS-ALIGNED AUTO-ENCODER)

EVALUATION

• Sentiment Modification

• Sentiment Accuracy

• Human Evaluation

• Word Substitution Decipherment

• Blue scores

• Word Order Recovery

• Blue scores

SENTIMENT MODIFICATION

EVALUATION