  Recent work in crosslingual semantic parsing has successfully applied machine
translation to localize accurate parsing to new languages. However, these
advances assume access to high-quality machine translation systems, and tools
such as word aligners, for all test languages. We remove these assumptions and
study cross-lingual semantic parsing as a zero-shot problem without parallel
data for 7 test languages (DE, ZH, FR, ES, PT, HI, TR). We propose a multi-task
encoder-decoder model to transfer parsing knowledge to additional languages
using only English-Logical form paired data and unlabeled, monolingual
utterances in each test language. We train an encoder to generate
language-agnostic representations jointly optimized for generating logical
forms or utterance reconstruction and against language discriminability. Our
system frames zero-shot parsing as a latent-space alignment problem and finds
that pre-trained models can be improved to generate logical forms with minimal
cross-lingual transfer penalty. Experimental results on Overnight and a new
executable version of MultiATIS++ find that our zero-shot approach performs
above back-translation baselines and, in some cases, approaches the supervised
upper bound.
