A Probabilistic Formulation of Unsupervised Text Style Transfer
Table 5: Comparison of gradient propagation method on the sentiment transfer task.
['[BOLD] Method', '[BOLD] train ELBO↑', '[BOLD] test ELBO↑', '[BOLD] Acc.', '[BOLD] BLEU [ITALIC] r', '[BOLD] BLEU [ITALIC] s', '[BOLD] PPLD1', '[BOLD] PPLD2']
While being much simpler, we show that the stop-gradient trick produces superior ELBO over Gumbel Softmax and REINFORCE. This result suggests that stopping gradient helps better optimize the likelihood objective under our probabilistic formulation in comparison with other optimization techniques that propagate gradients, which is counter-intuitive. A likely explanation is that as a gradient estimator, while clearly biased, stop-gradient has substantially reduced variance. In comparison with other techniques that offer reduced bias but extremely high variance when applied to our model class (which involves discrete sequences as latent variables), stop-gradient actually leads to better optimization of our objective because it achieves better balance of bias and variance overall.