Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

Amadeo Hernández, Rosa María Ortega-Mendoza (), Esaú Villatoro-Tello, César Joel Camacho-Bello and Obed Pérez-Cortés
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Amadeo Hernández: Laboratorio de Inteligencia Artificial, Universidad Politécnica de Tulancingo, Tulancingo 43629, Hidalgo, Mexico
Rosa María Ortega-Mendoza: Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Mineral de la Reforma 42184, Hidalgo, Mexico
Esaú Villatoro-Tello: Idiap Research Institute, 1920 Martigny, Switzerland
César Joel Camacho-Bello: Laboratorio de Inteligencia Artificial, Universidad Politécnica de Tulancingo, Tulancingo 43629, Hidalgo, Mexico
Obed Pérez-Cortés: Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Mineral de la Reforma 42184, Hidalgo, Mexico

Mathematics, 2024, vol. 12, issue 8, 1-22

Abstract: Human–robot interaction is becoming increasingly common to perform useful tasks in everyday life. From the human–machine communication perspective, achieving effective interaction in natural language is one challenge. To address it, natural language processing strategies have recently been used, commonly following a supervised machine learning framework. In this context, most approaches rely on the use of linguistic resources (e.g., taggers or embeddings), including training corpora. Unfortunately, such resources are scarce for some languages in specific domains, increasing the complexity of solution approaches. Motivated by these challenges, this paper explores deep learning methods for understanding natural language commands emitted to service robots that guide their movements in low-resource scenarios, defined by the use of Spanish and Nahuatl languages, for which linguistic resources are scarcely unavailable for this specific task. Particularly, we applied natural language understanding (NLU) techniques using deep neural networks and transformers-based models. As part of the research methodology, we introduced a labeled dataset of movement commands in the mentioned languages. The results show that models based on transformers work well to recognize commands (intent classification task) and their parameters (e.g., quantities and movement units) in Spanish, achieving a performance of 98.70% (accuracy) and 96.96% (F1) for the intent classification and slot-filling tasks, respectively). In Nahuatl, the best performance obtained was 93.5% (accuracy) and 88.57% (F1) in these tasks, respectively. In general, this study shows that robot movements can be guided in natural language through machine learning models using neural models and cross-lingual transfer strategies, even in low-resource scenarios.

Keywords: natural language understanding; intent classification; slot filling; deep learning models; service robots; low-resource domains; Nahuatl and Spanish utterances (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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