Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing

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 primar-ily 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 dem-onstrate how this direct guidance improves parsing from natural languages using fewer examples and less training. We evaluate our method on two datasets, MTOP and Multi-ATIS++SQL, establishing state-of-the-art re-sults 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 struc-ture in the latent space to improve semantic representation similarity.