Comparative Performance of Teacher-Student Cross-Lingual NER Models Versus Zero-Shot and Few-Shot Approaches on XLENT

Abstract

Abstract: Identifying user intents and their corresponding slots is the first step in the utterance interpretation pipeline of many task-oriented conversational AI systems. A multilingual system that does not adequately address unbalanced issues may provide unsatisfactory experiences for users who communicate in low-resource languages, limiting the system's usability. Since data collection of machine learning models for this task is time-consuming, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. However, the development of such models h

Research Question

How does the performance of teacher-student cross-lingual NER models compare to zero-shot and few-shot learning approaches on the XLENT benchmark when trained on diverse high-resource languages?

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