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    <abstract>The growing need for transparency in AI systems has led to the increased popularity of explainable AI (XAI), with dialogue systems emerging as a promising approach to provide dynamic and interactive explanations. To overcome the limitations of non-conversational XAI methods, we proposed and implemented a generic dialogue architecture that integrates domain-specific knowledge, enhancing user comprehension and interaction. By incorporating computational argumentation and argumentative tree structures into our prototype, we found a positive impact on the dialogue‘s effectiveness. In future research, we plan to improve Natural Language Understanding (NLU) to reduce error rates and better interpret user queries, and to advance Natural Language Generation (NLG) techniques for generating more fluid and contextually appropriate responses using large language models. Additionally, we will refine argument annotation to enable better selection and presentation of information, ensuring the system provides the most relevant and coherent explanations based on user needs. Over the next 5 to 10 years, we anticipate significant advancements in dialogue systems’ flexibility, personalization, and cultural adaptability, driven by large language models and open domain dialogues. These developments will enhance global communication, user satisfaction, and the effectiveness of virtual assistants across various applications while addressing ethical and social implications.</abstract>
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%0 Conference Proceedings
%T Interactive Explanations through Dialogue Systems
%A Feustel, Isabel
%Y Inoue, Koji
%Y Fu, Yahui
%Y Axelsson, Agnes
%Y Ohashi, Atsumoto
%Y Madureira, Brielen
%Y Zenimoto, Yuki
%Y Mohapatra, Biswesh
%Y Stricker, Armand
%Y Khosla, Sopan
%S Proceedings of the 20th Workshop of Young Researchers’ Roundtable on Spoken Dialogue Systems
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F feustel-2024-interactive
%X The growing need for transparency in AI systems has led to the increased popularity of explainable AI (XAI), with dialogue systems emerging as a promising approach to provide dynamic and interactive explanations. To overcome the limitations of non-conversational XAI methods, we proposed and implemented a generic dialogue architecture that integrates domain-specific knowledge, enhancing user comprehension and interaction. By incorporating computational argumentation and argumentative tree structures into our prototype, we found a positive impact on the dialogue‘s effectiveness. In future research, we plan to improve Natural Language Understanding (NLU) to reduce error rates and better interpret user queries, and to advance Natural Language Generation (NLG) techniques for generating more fluid and contextually appropriate responses using large language models. Additionally, we will refine argument annotation to enable better selection and presentation of information, ensuring the system provides the most relevant and coherent explanations based on user needs. Over the next 5 to 10 years, we anticipate significant advancements in dialogue systems’ flexibility, personalization, and cultural adaptability, driven by large language models and open domain dialogues. These developments will enhance global communication, user satisfaction, and the effectiveness of virtual assistants across various applications while addressing ethical and social implications.
%U https://aclanthology.org/2024.yrrsds-1.29/
%P 78-80

Markdown (Informal)

[Interactive Explanations through Dialogue Systems](https://aclanthology.org/2024.yrrsds-1.29/) (Feustel, YRRSDS 2024)

• Interactive Explanations through Dialogue Systems (Feustel, YRRSDS 2024)

ACL

• Isabel Feustel. 2024. Interactive Explanations through Dialogue Systems. In Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems, pages 78–80, Kyoto, Japan. Association for Computational Linguistics.