A persian benchmark for joint intent detection and slot filling

Mohades Khorasani, Ali; Akbari, Masoud; Karimi, Amirhosein; Saeedi, Tayyebeh; Saeidi, Zeinab; Ghezelbash, Kiana; Shamsezat, Fatemeh; Akbari, Mohammad

doi:10.22060/ajmc.2025.23058.1225

A persian benchmark for joint intent detection and slot filling

Articles in Press

Document Type : Original Article

Authors

Ali Mohades Khorasani

Masoud Akbari

Amirhosein Karimi

Tayyebeh Saeedi

Zeinab Saeidi

Kiana Ghezelbash

Fatemeh Shamsezat

Mohammad Akbari

Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran

10.22060/ajmc.2025.23058.1225

Abstract

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.

Keywords

ATIS dataset

Persian benchmark

Intent detection

Slot filling

Subjects