Natural language processing-based development of artificial intelligence-driven autonomous socially assistive robots
This study focuses on addressing the growing need for localized language support in socially assistive robots (SARs) due to rising labor costs and the limitations of human labor in developed countries. The research aims to develop a Turkish natural language processing (NLP) module to enhance SARs’ social interaction capabilities and integration into smart living spaces. By leveraging advanced machine learning models, specifically XLM-RoBERTa Large (deepset/xlm-roberta-large-squad2), the study evaluated cross-lingual transfer learning for Turkish question answering, addressing specific linguistic challenges, including agglutinative morphology and vowel harmony. The model was evaluated on the Turkish Question-Answering Dataset (TQuAD 2.0) with 2,520 validation examples, achieving 79.37% F1-score and 56.67% exact match score. The research established a methodological framework connecting adaptive NLP design principles with control systems theory, demonstrating how concepts from adaptive fuzzy control and robust neural adaptive control inform the development of more stable and reliable NLP systems for SAR applications. These outcomes highlight the potential of cross-lingual NLP models for SAR applications in Turkish-speaking environments. The research contributes to the field by: (i) evaluating cross-lingual transfer learning for Turkish SAR applications, (ii) demonstrating the effectiveness of XLM-RoBERTa for low-resource language adaptation, (iii) establishing a framework that connects adaptive NLP design with control systems theory for enhanced robustness, and (iv) identifying real-world SAE applications in healthcare, smart homes, and industrial settings. Future work will focus on integrating this NLP module with speech recognition and synthesis components for complete voice-interactive SAR systems.
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