Multilingual Natural Language Understanding (NLU) systems often struggle to adapt when new languages or new semantic labels are introduced with only a few annotated examples. This challenge is particularly pronounced for low-resource languages, where limited supervision and evolving label spaces make conventional joint-label classification approaches unstable. Most existing multilingual NLU models treat each language-semantic pair as an independent class, entangling linguistic and semantic representations and hindering few-shot adaptation. We propose Dual-Axis Compositional Few-Shot Learning, a framework that explicitly factorizes the representation space into linguistic and semantic embedding axes, enabling independent modeling of language variation and domain-intent semantics. Joint representations are constructed compositionally through multiplicative interaction of axis-specific embeddings, allowing controlled adaptation when either the language set or the semantic label space evolves. The framework integrates factorized prototype learning, axis-structured contrastive alignment, and disentanglement regularization using HSIC-based statistical independence and Jacobian-based cross-axis decorrelation. Experiments on six low-resource Indic languages spanning Indo-Aryan and Dravidian families (Hindi, Bengali, Sanskrit, Assamese, Tamil, and Telugu) demonstrate strong performance under two structured generalization regimes. The model achieves 81.12% accuracy when adapting to few-shot languages with known semantics and 63.5% accuracy when learning new semantic classes from few-shot examples, along with an accuracy of 89.56% on known language and seen semantics. These results show that axis-factorized representations enable stable compositional generalization, offering a promising direction for scalable multilingual NLU in linguistically diverse low-resource settings.

NLU (Natural Language Understanding) has considerable difficulties in identifying multiple intentions across different domains in languages with limited resources. Our contributions involve utilizing pivot languages with similar semantics for NLU tasks, creating a vector database for efficient retrieval and indexing of language embeddings in high-resource languages for Retrieval Augmented Generation (RAG) in low-resource languages, and thoroughly investigating the effect of segmentbased strategies on complex user utterances across multiple domains and intents in the development of a Chain of Thought Prompting (COT) combined with Retrieval Augmented Generation. The study investigated recursive approaches to identify the most effective zeroshot instances for segment-based prompting. A comparison analysis was conducted to compare the effectiveness of sentence-based prompting vs segment-based prompting across different domains and multiple intents. This research offers a promising avenue to address the formidable challenges of NLU in low-resource languages, with potential applications in conversational agents and dialogue systems and a broader impact on linguistic understanding and inclusivity.