How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction

Orme, Michael; Yu, Yanchao; Tan, Zhiyuan

Abstract

This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and robots transition from controlled laboratory settings to everyday households, the demand for polite and culturally sensitive conversational abilities becomes paramount, especially for younger individuals. This study explores data cleaning methods, focusing on rudeness and contextual similarity, to identify and mitigate inappropriate language in real-time interactions. State-of-the-art natural language models are also evaluated for their proficiency in discerning rudeness. This multifaceted investigation highlights the challenges of handling inappropriate language, including its tendency to hide within idiomatic expressions and its context-dependent nature. This study will further contribute to the future development of AI systems capable of engaging in intelligent conversations and upholding the values of courtesy and respect across diverse cultural and generational boundaries.

Citation

Orme, M., Yu, Y., & Tan, Z. (2024, May). How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction. Presented at The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
• Start Date: May 20, 2024
• End Date: May 25, 2024
• Acceptance Date: Feb 20, 2024
• Publication Date: 2024
• Deposit Date: Mar 26, 2024
• Publisher: European Language Resources Association (ELRA)
• Peer Reviewed: Peer Reviewed
• Pages: 8247-8257
• Book Title: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
• Keywords: Natural Language Processing; Human-Robot Interaction; Inappropriate Language
• Public URL: http://researchrepository.napier.ac.uk/Output/3576666
• Publisher URL: https://aclanthology.org/2024.lrec-main.723
• Related Public URLs: https://lrec-coling-2024.org/