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

Jader Camboim de Sá, Dimitra Anastasiou, Marcos Da Silveira, Cédric Pruski


Abstract
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.
Anthology ID:
2024.teicai-1.4
Volume:
Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)
Month:
March
Year:
2024
Address:
St Julians, Malta
Editors:
Nina Hosseini-Kivanani, Sviatlana Höhn, Dimitra Anastasiou, Bettina Migge, Angela Soltan, Doris Dippold, Ekaterina Kamlovskaya, Fred Philippy
Venues:
TEICAI | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–27
Language:
URL:
https://aclanthology.org/2024.teicai-1.4
DOI:
Bibkey:
Cite (ACL):
Jader Camboim de Sá, Dimitra Anastasiou, Marcos Da Silveira, and Cédric Pruski. 2024. Socio-cultural adapted chatbots: Harnessing Knowledge Graphs and Large Language Models for enhanced context awarenes. In Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024), pages 21–27, St Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
Socio-cultural adapted chatbots: Harnessing Knowledge Graphs and Large Language Models for enhanced context awarenes (Camboim de Sá et al., TEICAI-WS 2024)
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PDF:
https://aclanthology.org/2024.teicai-1.4.pdf
Video:
 https://aclanthology.org/2024.teicai-1.4.mp4