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.

翻译：自然语言理解在当今技术中至关重要，它使机器能够理解和处理人类语言，从而改善人机交互并推动虚拟助手、聊天机器人及基于语言的AI系统等领域的发展。本文强调了推动低资源语言NLU领域研究的重要性。意图检测和槽位填充作为NLU中的关键任务，过去广泛使用的ATIS和SNIPS数据集仅支持英语，无法适配其他语言。本研究旨在通过构建基于ATIS数据集的波斯语联合意图检测与槽位填充基准数据集来填补这一空白。为验证该基准的有效性，我们采用了当前最先进的意图检测和槽位填充方法进行评估。