An analysis of subtask-dependency in robot command interpretation with dilated CNNs
26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
In this paper, we tackle sequence-to-tree transduction for language processing with neural networks implementing several subtasks, namely tokenization, semantic annotation, and tree generation. Our research question is how the individual subtasks influence the overall end-to-end learning performance in case of a convolutional network with dilated perceptive fields. We investigate a benchmark problem for robot command interpretation and conclude that dilation has a strong positive effect for performing character-level transduction and for generating parsing trees.