  Natural Language Understanding (NLU) is important in today's technology as it
enables machines to comprehend and process human language, 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.
