Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing
Bo Shao, Yeyun Gong, Weizhen Qi, Nan Duan, Xiaola Lin · 2020
In this paper, we present a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing.In this architecture, we use an adversarial training method to align the space of different languages and use sentence level and word level parallel corpus as supervision information to align the semantic of different languages.Finally, we jointly train the multi-level alignment and semantic parsing tasks.We conduct experiments on a publicly available multi-lingual semantic parsing dataset ATIS and a newly constructed dataset.Experimental results show that our model outperforms state-of-the-art methods on both datasets.