Before revision: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . 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 collapsewith VAEs.
After revision: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning to provide a fixed length representation of variable length language observations (\ie . 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 collapsewith VAEs.