Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing
This addresses the problem of transferring parsing capability to low-resource languages with few-shot gold data, representing a novel method for a known bottleneck.
The paper tackles cross-lingual semantic parsing by minimizing cross-lingual divergence using Optimal Transport, achieving state-of-the-art results on MTOP and MultiATIS++SQL datasets with fewer examples and less training.
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, however, 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 less training. We evaluate our method on two datasets, MTOP and MultiATIS++SQL, establishing state-of-the-art results under a few-shot cross-lingual regime. Ablation studies further reveal that our method improves performance even without parallel input translations. In addition, we show that our model better captures cross-lingual structure in the latent space to improve semantic representation similarity.