LU4R: Adaptive Spoken Language Understanding for Robots

Service robots are expected to operate in specific environments, where the presence of humans plays a key role. It is thus essential to enable for a natural and effective communication among humans and robots. One of the main features of such robotics platforms is the ability to react to spoken comm...

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Autores principales:	Andrea Vanzo, Roberto Basili, Danilo Croce, Daniele Nardi
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2017
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/5c4f58427f53494a9141a24ac9522d07
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spelling	oai:doaj.org-article:5c4f58427f53494a9141a24ac9522d072021-12-02T09:52:27ZLU4R: Adaptive Spoken Language Understanding for Robots2499-455310.4000/ijcol.432https://doaj.org/article/5c4f58427f53494a9141a24ac9522d072017-06-01T00:00:00Zhttp://journals.openedition.org/ijcol/432https://doaj.org/toc/2499-4553Service robots are expected to operate in specific environments, where the presence of humans plays a key role. It is thus essential to enable for a natural and effective communication among humans and robots. One of the main features of such robotics platforms is the ability to react to spoken commands. This requires a comprehensive understanding of the user utterance to trigger the robot reaction. Moreover, the correct interpretation of linguistic interactions depends on physical, cognitive and language-dependent aspects related to the environment. In this work, we present the latest version of LU4R - adaptive spoken Language Understanding 4 Robots, a Spoken Language Understanding framework for the semantic interpretation of robotic commands, that is sensitive to the operational environment. The overall system is designed according to a Client/Server architecture in order to be easily deployed in a vast plethora of robotic platforms. Moreover, an improved version of HuRIC - Human-Robot Interaction Corpus is presented. The main novelty presented in this paper is the extension to commands expressed in Italian. In order to prove the effectiveness of such system, we also present some empirical results in both English and Italian computed over the new HuRIC resource.Andrea VanzoRoberto BasiliDanilo CroceDaniele NardiAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 3, Iss 1, Pp 59-76 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social Sciences H Computational linguistics. Natural language processing P98-98.5
spellingShingle	Social Sciences H Computational linguistics. Natural language processing P98-98.5 Andrea Vanzo Roberto Basili Danilo Croce Daniele Nardi LU4R: Adaptive Spoken Language Understanding for Robots
description	Service robots are expected to operate in specific environments, where the presence of humans plays a key role. It is thus essential to enable for a natural and effective communication among humans and robots. One of the main features of such robotics platforms is the ability to react to spoken commands. This requires a comprehensive understanding of the user utterance to trigger the robot reaction. Moreover, the correct interpretation of linguistic interactions depends on physical, cognitive and language-dependent aspects related to the environment. In this work, we present the latest version of LU4R - adaptive spoken Language Understanding 4 Robots, a Spoken Language Understanding framework for the semantic interpretation of robotic commands, that is sensitive to the operational environment. The overall system is designed according to a Client/Server architecture in order to be easily deployed in a vast plethora of robotic platforms. Moreover, an improved version of HuRIC - Human-Robot Interaction Corpus is presented. The main novelty presented in this paper is the extension to commands expressed in Italian. In order to prove the effectiveness of such system, we also present some empirical results in both English and Italian computed over the new HuRIC resource.
format	article
author	Andrea Vanzo Roberto Basili Danilo Croce Daniele Nardi
author_facet	Andrea Vanzo Roberto Basili Danilo Croce Daniele Nardi
author_sort	Andrea Vanzo
title	LU4R: Adaptive Spoken Language Understanding for Robots
title_short	LU4R: Adaptive Spoken Language Understanding for Robots
title_full	LU4R: Adaptive Spoken Language Understanding for Robots
title_fullStr	LU4R: Adaptive Spoken Language Understanding for Robots
title_full_unstemmed	LU4R: Adaptive Spoken Language Understanding for Robots
title_sort	lu4r: adaptive spoken language understanding for robots
publisher	Accademia University Press
publishDate	2017
url	https://doaj.org/article/5c4f58427f53494a9141a24ac9522d07
work_keys_str_mv	AT andreavanzo lu4radaptivespokenlanguageunderstandingforrobots AT robertobasili lu4radaptivespokenlanguageunderstandingforrobots AT danilocroce lu4radaptivespokenlanguageunderstandingforrobots AT danielenardi lu4radaptivespokenlanguageunderstandingforrobots
_version_	1718397947981332480

LU4R: Adaptive Spoken Language Understanding for Robots

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