Amirkabir University of Technology AUT Journal of Mathematics and Computing 2783-2449 Articles in Press 2025 01 29 A persian benchmark for joint intent detection and slot filling 5666 10.22060/ajmc.2025.23058.1225 EN Ali Mohades Khorasani Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran 0000-0002-6118-2245 Masoud Akbari Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran 0000-0002-7691-0778 Amirhosein Karimi Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Tayyebeh Saeedi Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Zeinab Saeidi Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Kiana Ghezelbash Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Fatemeh Shamsezat Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Mohammad Akbari Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran Journal Article 2024 03 19 Abstract: Natural Language Understanding (NLU) is important in today’s technology as it enables machines to comprehend and process human languages, leading to improved human-computer interactions and advancements in fields such as virtual assistants, chatbots, and language-based AI systems. This paper highlights the significance of advancing the field of NLU for low-resource languages. With intent detection and slot filling being crucial tasks in NLU, the widely used datasets ATIS and SNIPS have been utilized in the past. However, these datasets only cater to the English language and do not support other languages. In this work, we aim to address this gap by creating a Persian benchmark for joint intent detection and slot filling based on the ATIS dataset. To evaluate the effectiveness of our benchmark, we employ state-of-the-art methods for intent detection and slot filling.