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Semantic Role Labelling for Robot Instructions using Echo State Networks
Abstract : To control a robot in a real-world robot scenario, a real-time parser is needed to create semantic representations from natural language which can be interpreted. The parser should be able to create the hierarchical tree-like representations without consulting external systems to show its learning capabilities. We propose an efficient Echo State Network-based parser for robotic commands and only relies on the training data. The system generates a single semantic tree structure in real-time which can be executed by a robot arm manipulating objects. Four of six other approaches, which in most cases generate multiple trees and select one of them as the solution, were outperformed with 64.2% tree accuracy on difficult unseen natural language (74.1% under best conditions) on the same dataset.
Cited literature [18 references]
https://hal.inria.fr/hal-01417701
Contributor : Xavier Hinaut <>
Submitted on : Thursday, December 15, 2016 - 7:00:32 PM
Last modification on : Sunday, November 22, 2020 - 12:34:06 PM
Contributor : Xavier Hinaut <>
Submitted on : Thursday, December 15, 2016 - 7:00:32 PM
Last modification on : Sunday, November 22, 2020 - 12:34:06 PM
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