Languages continually evolve in response to societal events, resulting in new terms and shifts in meanings. These changes have significant implications for computer applications, including automatic translation and chatbots, making it essential to characterize them accurately. The recent development of LLMs has notably advanced natural language understanding, particularly in sense inference and reasoning. In this paper, we investigate the potential of LLMs in characterizing three types of semantic change: dimension, relation, and orientation. We achieve this by combining LLMs’ Chain-of-Thought with rhetorical devices and conducting an experimental assessment of our approach using newly created datasets. Our results highlight the effectiveness of LLMs in capturing and analyzing semantic changes, providing valuable insights to improve computational linguistic applications.

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.