We use the posteriors to re-estimate LM parameters as follows To obtain better parameter estimates for word predictions and avoid overfitting, we use smoothing in the M-step.
though they also consider divergences between distributions of latent variable vectors, they use these divergences at learning time to bias models to induce representations maximally invariant across domains.