@inproceedings{camboim-de-sa-etal-2024-socio,
title = "Socio-cultural adapted chatbots: Harnessing Knowledge Graphs and Large Language Models for enhanced context awarenes",
author = "Camboim de S{\'a}, Jader and
Anastasiou, Dimitra and
Da Silveira, Marcos and
Pruski, C{\'e}dric",
editor = {Hosseini-Kivanani, Nina and
H{\"o}hn, Sviatlana and
Anastasiou, Dimitra and
Migge, Bettina and
Soltan, Angela and
Dippold, Doris and
Kamlovskaya, Ekaterina and
Philippy, Fred},
booktitle = "Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)",
month = mar,
year = "2024",
address = "St Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.teicai-1.4",
pages = "21--27",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Socio-cultural adapted chatbots: Harnessing Knowledge Graphs and Large Language Models for enhanced context awarenes
%A Camboim de Sá, Jader
%A Anastasiou, Dimitra
%A Da Silveira, Marcos
%A Pruski, Cédric
%Y Hosseini-Kivanani, Nina
%Y Höhn, Sviatlana
%Y Anastasiou, Dimitra
%Y Migge, Bettina
%Y Soltan, Angela
%Y Dippold, Doris
%Y Kamlovskaya, Ekaterina
%Y Philippy, Fred
%S Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St Julians, Malta
%F camboim-de-sa-etal-2024-socio
%X 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.
%U https://aclanthology.org/2024.teicai-1.4
%P 21-27
Markdown (Informal)
[Socio-cultural adapted chatbots: Harnessing Knowledge Graphs and Large Language Models for enhanced context awarenes](https://aclanthology.org/2024.teicai-1.4) (Camboim de Sá et al., TEICAI-WS 2024)
ACL