Deep Tree Transductions - A Short Survey
This is an incremental survey paper for researchers in machine learning and natural language processing.
This survey examines extensions of LSTM networks for tree structures, analyzing TreeLSTM models and their processing direction biases, and finds no single model effectively handles all tree transduction problems based on empirical benchmarks.
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.