Optimal Transport Posterior Alignment for Few-Shot Cross-Lingual Transfer with Limited Gold Data

Abstract

Abstract: Abstract Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. Previous work has primarily considered silver-standard data augmentation or zero-shot methods; exploiting few-shot gold data is comparatively unexplored. We propose a new approach to cross-lingual semantic parsing by explicitly minimizing cross-lingual divergence between probabilistic latent variables using Optimal Transport. We demonstrate how this direct guidance improves parsing from natural languages using fewer examples and

Research Question

What is the impact of Optimal Transport Posterior Alignment on few-shot cross-lingual transfer performance compared to zero-shot methods when limited gold data is available?

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