A Persian Benchmark for Joint Intent Detection and Slot Filling

TL;DR

This paper introduces a new Persian benchmark dataset for joint intent detection and slot filling, addressing the lack of resources for low-resource languages in NLU, and evaluates it using current state-of-the-art methods.

Contribution

The paper presents the first Persian dataset for joint intent detection and slot filling, expanding NLU resources beyond English.

Findings

State-of-the-art methods perform effectively on the Persian benchmark.

The dataset facilitates research in Persian NLU tasks.

Baseline results establish a foundation for future improvements.

Abstract

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.