A persian benchmark for joint intent detection and slot filling | ||
| AUT Journal of Mathematics and Computing | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از 10 بهمن 1403 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajmc.2025.23058.1225 | ||
| نویسندگان | ||
| 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 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| ATIS dataset؛ Persian benchmark؛ Intent detection؛ Slot filling | ||
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