 Previous attempts to learn variational auto-encoders for language data ? have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the ? occurrence of posterior collapse with VAEs.
<fluency>  Previous attempts to learn variational auto-encoders for language data ? have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the ? occurrence of posterior collapse with VAEs.
 Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the ? occurrence of posterior collapse with VAEs.