In Figure 3, we present the validation perplexity to compare the abilities of the three models to learn the compression languages.
for the parameters φ in the compression model, we firstly define the learning signal, Then, we update the parameters φ by: this gradient estimator has a big variance because the learning signal l(s, c (m) ) relies on the samples from q φ (c|s).