Integrating Model-Agnostic Meta-Learning with Advanced Language Embeddings for Few-Shot Intent Classification
Ali Rahimi, Hadi Veisi · 2024
Addressing the challenge of few-shot learning in intent classification tasks within Natural Language Processing (NLP), this study introduces a novel approach that harnesses the robust adaptation capabilities of Model-Agnostic Meta-Learning (MAML) combined with sophisticated language embeddings, namely BERT, LaBSE, and text-embedding-ada-002. The need for models to understand and classify intents with minimal training data is imperative to progress in creating versatile, responsive AI systems. We propose a methodology that leverages the generalizability of MAML and the deeply contextualized representations offered by state-of-the-art embeddings, allowing for significant improvements in Accuracy and data efficiency. We evaluate our approach using the CLINC150 dataset across a series of N-way & K-shot configurations, demonstrating the efficacy of the proposed model with varying numbers of in-tent classes and examples. Our findings reveal that the text-embedding-ada-002 embeddings consistently provide superior performance in both 1-shot and 5-shot settings across all class configurations tested, indicating their potent synergy with meta-learning strategies. Specifically, text-embedding-ada-002 achieved an accuracy of 97.07% in the 5-Way & 1-Shot setting and 99.1% in the 5-Way & 5-Shot setting. The outcomes of our experimental evaluation suggest that our approach also illuminates the potential of harmonious integration of cutting-edge language embeddings with meta-learning frameworks. This work provides a solid foundation for further exploration in optimizing few-shot intent classification, paving the way for creating AI systems proficient in understanding user intents with minimal exemplars. This research lays the groundwork for future advancements in few-shot intent classification, enabling the development of AI systems that require minimal training data to interpret user intent accurately.