A Persian Benchmark for Joint Intent Detection and Slot Filling
This work addresses a gap for Persian language processing, but it is incremental as it adapts an existing English dataset to a new language.
The authors tackled the lack of datasets for low-resource languages in natural language understanding by creating a Persian benchmark for joint intent detection and slot filling, based on the ATIS dataset, and evaluated it using state-of-the-art methods.
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.