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    <abstract>Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user’s question by using a BERT classification model which labels the question with a dialog node label.</abstract>
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%0 Conference Proceedings
%T ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot
%A Anastasiou, Dimitra
%Y Melero, Maite
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F anastasiou-2022-enrich4all
%X Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user’s question by using a BERT classification model which labels the question with a dialog node label.
%U https://aclanthology.org/2022.sigul-1.27
%P 207-212

Markdown (Informal)

[ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot](https://aclanthology.org/2022.sigul-1.27) (Anastasiou, SIGUL 2022)

• ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot (Anastasiou, SIGUL 2022)

ACL

• Dimitra Anastasiou. 2022. ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot. In Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, pages 207–212, Marseille, France. European Language Resources Association.