Modern virtual assistants use internal semantic parsing engines to convert
user utterances to actionable commands. However, prior work has demonstrated
that semantic parsing is a difficult multilingual transfer task with low
transfer efficiency compared to other tasks. In global markets such as India
and Latin America, this is a critical issue as switching between languages is
prevalent for bilingual users. In this work we dramatically improve the
zero-shot performance of a multilingual and codeswitched semantic parsing
system using two stages of multilingual alignment. First, we show that
constrastive alignment pretraining improves both English performance and
transfer efficiency. We then introduce a constrained optimization approach for
hyperparameter-free adversarial alignment during finetuning. Our Doubly Aligned
Multilingual Parser (DAMP) improves mBERT transfer performance by 3x, 6x, and
81x on the Spanglish, Hinglish and Multilingual Task Oriented Parsing
benchmarks respectively and outperforms XLM-R and mT5-Large using 3.2x fewer
parameters.

使用两个阶段的多语言语义解析的多语言代码切换语义解析系统，通过对比预训练改进英语性能和转移效率，并引入有限调整优化方法来提高零 - shot 性能，将 mBERT 的转移性能大大提高，同时使用 3.2 倍少的参数优于 XLM-R 和 mT5-Large。