Socio-cultural adapted chatbots: Harnessing Knowledge Graphs and Large Language Models for enhanced context awareness

Authors

de Sá J.M.C., Anastasiou D., Da Silveira M., Pruski C.

Reference

TEICAI 2024 - 1st Workshop Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights, Proceedings of the Workshop, pp. 21-27, 2024

Description

Understanding the socio-cultural context is crucial in machine translation (MT). Although conversational AI systems and chatbots, in particular, are not designed for translation, they can be used for MT purposes. Yet, chatbots often struggle to identify any socio-cultural context during user interactions. In this paper, we highlight this challenge with real-world examples from popular chatbots. We advocate for the use of knowledge graphs as an external source of information that can potentially encapsulate socio-cultural contexts, aiding chatbots in enhancing translation. We further present a method to exploit external knowledge and extract contextual information that can significantly improve text translation, as evidenced by our interactions with these chatbots.