An analysis of subtask-dependency in robot command interpretation with dilated CNNs

Publikationstyp
Conference Paper
Date Issued
2018-04
Sprache
English
Author(s)
Eppe, Manfred  
Alpay, Tayfun  
Abawi, Fares  
Wermter, Stefan  
TORE-URI
http://hdl.handle.net/11420/12364
Start Page
25
End Page
30
Citation
26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
Contribution to Conference
26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018  
Scopus ID
2-s2.0-85062937570
ISBN of container
978-287587047-6
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.