  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.
