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        <title>Task-Oriented Dialog Systems for the Senegalese Wolof Language</title>
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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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            <namePart type="given">Owen</namePart>
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            <namePart type="given">Marianna</namePart>
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    <abstract>In recent years, we are seeing considerable interest in conversational agents with the rise of large language models (LLMs). Although they offer considerable advantages, LLMs also present significant risks, such as hallucination, which hinder their widespread deployment in industry. Moreover, low-resource languages such as African ones are still underrepresented in these systems limiting their performance in these languages. In this paper, we illustrate a more classical approach based on modular architectures of Task-oriented Dialog Systems (ToDS) offering better control over outputs. We propose a chatbot generation engine based on the Rasa framework and a robust methodology for projecting annotations onto the Wolof language using an in-house machine translation system. After evaluating a generated chatbot trained on the Amazon Massive dataset, our Wolof Intent Classifier performs similarly to the one obtained for French, which is a resource-rich language. We also show that this approach is extensible to other low-resource languages, thanks to the intent classifier‘s language-agnostic pipeline, simplifying the design of chatbots in these languages.</abstract>
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%0 Conference Proceedings
%T Task-Oriented Dialog Systems for the Senegalese Wolof Language
%A Mbaye, Derguene
%A Diallo, Moussa
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F mbaye-diallo-2025-task
%X In recent years, we are seeing considerable interest in conversational agents with the rise of large language models (LLMs). Although they offer considerable advantages, LLMs also present significant risks, such as hallucination, which hinder their widespread deployment in industry. Moreover, low-resource languages such as African ones are still underrepresented in these systems limiting their performance in these languages. In this paper, we illustrate a more classical approach based on modular architectures of Task-oriented Dialog Systems (ToDS) offering better control over outputs. We propose a chatbot generation engine based on the Rasa framework and a robust methodology for projecting annotations onto the Wolof language using an in-house machine translation system. After evaluating a generated chatbot trained on the Amazon Massive dataset, our Wolof Intent Classifier performs similarly to the one obtained for French, which is a resource-rich language. We also show that this approach is extensible to other low-resource languages, thanks to the intent classifier‘s language-agnostic pipeline, simplifying the design of chatbots in these languages.
%U https://aclanthology.org/2025.coling-main.322/
%P 4803-4812

Markdown (Informal)

[Task-Oriented Dialog Systems for the Senegalese Wolof Language](https://aclanthology.org/2025.coling-main.322/) (Mbaye & Diallo, COLING 2025)

• Task-Oriented Dialog Systems for the Senegalese Wolof Language (Mbaye & Diallo, COLING 2025)

ACL

• Derguene Mbaye and Moussa Diallo. 2025. Task-Oriented Dialog Systems for the Senegalese Wolof Language. In Proceedings of the 31st International Conference on Computational Linguistics, pages 4803–4812, Abu Dhabi, UAE. Association for Computational Linguistics.