  In this work, we focus on a lightweight convolutional architecture that
creates fixed-size vector embeddings of sentences. Such representations are
useful for building NLP systems, including conversational agents. Our work
derives from a recently proposed recursive convolutional architecture for
auto-encoding text paragraphs at byte level. We propose alternations that
significantly reduce training time, the number of parameters, and improve
auto-encoding accuracy. Finally, we evaluate the representations created by our
model on tasks from SentEval benchmark suite, and show that it can serve as a
better, yet fairly low-resource alternative to popular bag-of-words embeddings.
