@inproceedings{amadeus-etal-2024-bridging,
title = "Bridging the Language Gap: Integrating Language Variations into Conversational {AI} Agents for Enhanced User Engagement",
author = "Amadeus, Marcellus and
Homeli da Silva, Jose Roberto and
Pessoa Rocha, Joao Victor",
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-1.3",
pages = "16--20",
abstract = "This paper presents the initial steps taken to integrate language variations into conversational AI agents to enhance user engagement. The study is built upon sociolinguistic and pragmatic traditions and involves the creation of an annotation taxonomy. The taxonomy includes eleven classes, ranging from concrete to abstract, and the covered aspects are the instance itself, time, sentiment, register, state, region, type, grammar, part of speech, meaning, and language. The paper discusses the challenges of incorporating vernacular language into AI agents, the procedures for data collection, and the taxonomy organization. It also outlines the next steps, including the database expansion and the computational implementation. The authors believe that integrating language variation into conversational AI will build near-real language inventories and boost user engagement. The paper concludes by discussing the limitations and the importance of building rapport with users through their own vernacular.",
}
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<abstract>This paper presents the initial steps taken to integrate language variations into conversational AI agents to enhance user engagement. The study is built upon sociolinguistic and pragmatic traditions and involves the creation of an annotation taxonomy. The taxonomy includes eleven classes, ranging from concrete to abstract, and the covered aspects are the instance itself, time, sentiment, register, state, region, type, grammar, part of speech, meaning, and language. The paper discusses the challenges of incorporating vernacular language into AI agents, the procedures for data collection, and the taxonomy organization. It also outlines the next steps, including the database expansion and the computational implementation. The authors believe that integrating language variation into conversational AI will build near-real language inventories and boost user engagement. The paper concludes by discussing the limitations and the importance of building rapport with users through their own vernacular.</abstract>
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%0 Conference Proceedings
%T Bridging the Language Gap: Integrating Language Variations into Conversational AI Agents for Enhanced User Engagement
%A Amadeus, Marcellus
%A Homeli da Silva, Jose Roberto
%A Pessoa Rocha, Joao Victor
%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 amadeus-etal-2024-bridging
%X This paper presents the initial steps taken to integrate language variations into conversational AI agents to enhance user engagement. The study is built upon sociolinguistic and pragmatic traditions and involves the creation of an annotation taxonomy. The taxonomy includes eleven classes, ranging from concrete to abstract, and the covered aspects are the instance itself, time, sentiment, register, state, region, type, grammar, part of speech, meaning, and language. The paper discusses the challenges of incorporating vernacular language into AI agents, the procedures for data collection, and the taxonomy organization. It also outlines the next steps, including the database expansion and the computational implementation. The authors believe that integrating language variation into conversational AI will build near-real language inventories and boost user engagement. The paper concludes by discussing the limitations and the importance of building rapport with users through their own vernacular.
%U https://aclanthology.org/2024.teicai-1.3
%P 16-20
Markdown (Informal)
[Bridging the Language Gap: Integrating Language Variations into Conversational AI Agents for Enhanced User Engagement](https://aclanthology.org/2024.teicai-1.3) (Amadeus et al., TEICAI-WS 2024)
ACL