  Tree data occurs in many forms, such as computer programs, chemical
molecules, or natural language. Unfortunately, the non-vectorial and discrete
nature of trees makes it challenging to construct functions with tree-formed
output, complicating tasks such as optimization or time series prediction.
Autoencoders address this challenge by mapping trees to a vectorial latent
space, where tasks are easier to solve, and then mapping the solution back to a
tree structure. However, existing autoencoding approaches for tree data fail to
take the specific grammatical structure of tree domains into account and rely
on deep learning, thus requiring large training datasets and long training
times. In this paper, we propose tree echo state autoencoders (TES-AE), which
are guided by a tree grammar and can be trained within seconds by virtue of
reservoir computing. In our evaluation on three datasets, we demonstrate that
our proposed approach is not only much faster than a state-of-the-art deep
learning autoencoding approach (D-VAE) but also has less autoencoding error if
little data and time is given.
