An Analysis of Subtask-Dependency in Robot Command Interpretation with Dilated CNNs

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-toend 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.

@InProceedings{EAAW18, 
 	 author =  {Eppe, Manfred and Alpay, Tayfun and Abawi, Fares and Wermter, Stefan},  
 	 title = {An Analysis of Subtask-Dependency in Robot Command Interpretation with Dilated CNNs}, 
 	 booktitle = {Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)},
 	 number = {},
 	 volume = {},
 	 pages = {25--30},
 	 year = {2018},
 	 month = {Apr},
 	 publisher = {},
 	 doi = {}, 
 }