Semantic Role Labelling for Robot Instructions using Echo State Networks

Johannes Twiefel 1 Xavier Hinaut 1, 2 Stefan Wermter 1
1 KT - Knowledge Technology group [Hamburg]
Department of Informatics [Hamburg]
2 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
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.
Type de document :
Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2016, Bruges, Belgium. 〈https://www.elen.ucl.ac.be/esann/index.php?pg=esann16_programme〉
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01417701
Contributeur : Xavier Hinaut <>
Soumis le : jeudi 15 décembre 2016 - 19:00:32
Dernière modification le : mercredi 28 mars 2018 - 13:24:02

Fichier

Twiefel_ESANN_2016_camera_read...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01417701, version 1

Collections

Citation

Johannes Twiefel, Xavier Hinaut, Stefan Wermter. Semantic Role Labelling for Robot Instructions using Echo State Networks. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2016, Bruges, Belgium. 〈https://www.elen.ucl.ac.be/esann/index.php?pg=esann16_programme〉. 〈hal-01417701〉

Partager

Métriques

Consultations de la notice

310

Téléchargements de fichiers

82