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arXiv · 1811.05242

A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

Martino Mensio·Emanuele Bastianelli·Ilaria Tiddi·Giuseppe Rizzo
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Abstract

As the first robotic platforms slowly approach our everyday life, we can imagine a near future where service robots will be easily accessible by non-expert users through vocal interfaces. The capability of managing natural language would indeed speed up the process of integrating such platform in the ordinary life. Semantic parsing is a fundamental task of the Natural Language Understanding process, as it allows extracting the meaning of a user utterance to be used by a machine. In this paper, we present a preliminary study to semantically parse user vocal commands for a House Service robot, using a multi-layer Long-Short Term Memory neural network with attention mechanism. The system is trained on the Human Robot Interaction Corpus, and it is preliminarily compared with previous approaches.

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Martino Mensio, Emanuele Bastianelli, Ilaria Tiddi, Giuseppe Rizzo. 2018-11-13. A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling. https://arxiv.org/abs/1811.05242

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