Deep Tree Transductions - A Short Survey
Machine Learning
2019-02-06 v1 Neural and Evolutionary Computing
Machine Learning
Abstract
The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.
Keywords
Cite
@article{arxiv.1902.01737,
title = {Deep Tree Transductions - A Short Survey},
author = {Davide Bacciu and Antonio Bruno},
journal= {arXiv preprint arXiv:1902.01737},
year = {2019}
}
Comments
To appear in the Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019). arXiv admin note: text overlap with arXiv:1809.09096