This research proposes a Margin-based Class Alignment (MCA) framework to address challenges in cross-lingual intent detection by optimizing adaptive angular margins and language-agnostic prototype learning, improving semantic alignment and discrimination between intent classes.
Key findings
MCA framework improves cross-lingual intent detection through adaptive margin optimization and language-agnostic prototype learning.
Adaptive margin loss dynamically adjusts inter-class separation based on cross-lingual semantic similarity.
Class-aware contrastive learning aligns intent representations across languages while preserving discriminative class boundaries.
Hard negative mining strategy identifies challenging cross-lingual examples to improve model robustness.
Limitations & open questions
The framework's effectiveness in extremely low-resource languages is yet to be evaluated.
The impact of MCA on real-world deployment and user experience remains to be seen.