Robots are slowly becoming part of everyday life, as they are being marketed for commercial applications (viz. telepresence, cleaning or entertainment). Thus, the ability to interact with non-expert users is becoming a key requirement. Even if user utterances can be efficiently recognized and transcribed by Automatic Speech Recognition systems, several issues arise in translating them into suitable robotic actions. In this paper, we will discuss both approaches providing two existing Natural Language Understanding workflows for Human Robot Interaction. First, we discuss a grammar based approach: it is based on grammars thus recognizing a restricted set of commands. Then, a data driven approach, based on a free-from speech recognizer and a statistical semantic parser, is discussed. The main advantages of both approaches are discussed, also from an engineering perspective, i.e. considering the effort of realizing HRI systems, as well as their reusability and robustness. An empirical evaluation of the proposed approaches is carried out on several datasets, in order to understand performances and identify possible improvements towards the design of NLP components in HRI.

Bastianelli, E., Castellucci, G., Croce, D., Basili, R., & Nardi, D. (2014). Effective and Robust Natural Language Understanding for Human Robot Interaction. In Frontiers in Artificial Intelligence and Applications (pp.57-62). ;Nieuwe Hemweg 6B : IOS Press [10.3233/978-1-61499-419-0-57].

Effective and Robust Natural Language Understanding for Human Robot Interaction

CROCE, DANILO;BASILI, ROBERTO;
2014

Abstract

Robots are slowly becoming part of everyday life, as they are being marketed for commercial applications (viz. telepresence, cleaning or entertainment). Thus, the ability to interact with non-expert users is becoming a key requirement. Even if user utterances can be efficiently recognized and transcribed by Automatic Speech Recognition systems, several issues arise in translating them into suitable robotic actions. In this paper, we will discuss both approaches providing two existing Natural Language Understanding workflows for Human Robot Interaction. First, we discuss a grammar based approach: it is based on grammars thus recognizing a restricted set of commands. Then, a data driven approach, based on a free-from speech recognizer and a statistical semantic parser, is discussed. The main advantages of both approaches are discussed, also from an engineering perspective, i.e. considering the effort of realizing HRI systems, as well as their reusability and robustness. An empirical evaluation of the proposed approaches is carried out on several datasets, in order to understand performances and identify possible improvements towards the design of NLP components in HRI.
21st European Conference on Artificial Intelligence, ECAI 2014
cze
2014
Artificial Intelligence Journal
Rilevanza internazionale
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore INF/01 - Informatica
English
Artificial Intelligence; Artificial Intelligence
http://www.iospress.nl/loadtop/load.php?isbn=19057415
Intervento a convegno
Bastianelli, E., Castellucci, G., Croce, D., Basili, R., & Nardi, D. (2014). Effective and Robust Natural Language Understanding for Human Robot Interaction. In Frontiers in Artificial Intelligence and Applications (pp.57-62). ;Nieuwe Hemweg 6B : IOS Press [10.3233/978-1-61499-419-0-57].
Bastianelli, E; Castellucci, G; Croce, D; Basili, R; Nardi, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/124033
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