  We present a comprehensive study on the use of autoencoders for modelling
text data, in which (differently from previous studies) we focus our attention
on the following issues: i) we explore the suitability of two different models
bDA and rsDA for constructing deep autoencoders for text data at the sentence
level; ii) we propose and evaluate two novel metrics for better assessing the
text-reconstruction capabilities of autoencoders; and iii) we propose an
automatic method to find the critical bottleneck dimensionality for text
language representations (below which structural information is lost).
