Review for NeurIPS paper: Latent Template Induction with Gumbel-CRFs

Summary and Contributions: Neural template models [1, 2] are interesting with interpretability and controllability. Such models can be trained in a VAE framework with CRF as the posterior. Due to the non-differentiability of discrete templates, this paper investigates using Gumbel-Softmax as the gradient estimator for the posterior distribution against other gradient estimators such as REINFORCE and PM-MRF used by previous work. Empirically, the gumbel estimator demonstrates lower variance and better performance on unsupervised paraphrasing and data-to-text generation than comparable baselines. ACL 2020 ------------ After Rebuttal -------------- Thank you for the response!