Modern robotic architectures are equipped with sensors enabling a deep analysis of the environment. In this work, we aim at demonstrating that such perceptual information (here modeled through semantic maps) can be effectively used to enhance the language understanding capabilities of the robot. A robust lexical mapping function based on the Distributional Semantics paradigm is here proposed as a basic model of grounding language towards the environment. We show that making such information available to the underlying language understanding algorithms improves the accuracy throughout the entire interpretation process.
Using Semantic Maps for Robust Natural Language Interaction with Robots / Bastianelli, Emanuele; Danilo, Croce; Roberto, Basili; Nardi, Daniele. - STAMPA. - (2015), pp. 1393-1397. (Intervento presentato al convegno 16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 tenutosi a Dresden; Germany).
Using Semantic Maps for Robust Natural Language Interaction with Robots
BASTIANELLI, EMANUELE;NARDI, Daniele
2015
Abstract
Modern robotic architectures are equipped with sensors enabling a deep analysis of the environment. In this work, we aim at demonstrating that such perceptual information (here modeled through semantic maps) can be effectively used to enhance the language understanding capabilities of the robot. A robust lexical mapping function based on the Distributional Semantics paradigm is here proposed as a basic model of grounding language towards the environment. We show that making such information available to the underlying language understanding algorithms improves the accuracy throughout the entire interpretation process.File | Dimensione | Formato | |
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