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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