Intent Recognition with Low Resource Adaptation in Minority Language Services

Abstract

The rapid advancement of natural language processing technologies has revolutionized human-computer interaction, primarily through sophisticated intent recognition systems. However, these advancements remain disproportionately concentrated within high-resource languages, leaving minority and marginalized language communities with substandard digital services. This paper presents a comprehensive benchmark study that bridges the gap between low-resource adaptation techniques and practical intent recognition for minority language services. By constructing and evaluating a robust benchmark across multiple low-resource languages, this research investigates the efficacy of various cross-lingual transfer methodologies, data augmentation strategies, and few-shot learning paradigms. The core objective is to determine how limited linguistic resources can be optimally leveraged to build accurate and resilient intent classification systems. Through rigorous empirical analysis, this study demonstrates that advanced representation alignment and structurally aware adaptation frameworks significantly enhance intent recognition accuracy, even when training data is severely constrained. The findings provide critical evidence for the necessity of tailored algorithmic interventions rather than direct zero-shot applications from high-resource models. Ultimately, this work offers a foundational framework for researchers and service providers aiming to develop inclusive digital platforms, ensuring that minority language speakers receive equitable access to automated linguistic services.