Recent work in cross-lingual semantic parsing has successfully applied
machine translation to localize parsers to new languages. However, these
advances assume access to high-quality machine translation systems and word
alignment tools. We remove these assumptions and study cross-lingual semantic
parsing as a zero-shot problem, without parallel data (i.e., utterance-logical
form pairs) for new languages. We propose a multi-task encoder-decoder model to
transfer parsing knowledge to additional languages using only English-logical
form paired data and in-domain natural language corpora in each new language.
Our model encourages language-agnostic encodings by jointly optimizing for
logical-form generation with auxiliary objectives designed for cross-lingual
latent representation alignment. Our parser performs significantly above
translation-based baselines and, in some cases, competes with the supervised
upper-bound.

本文提出一种多任务编码 - 解码模型，通过多语言领域内语料库和英文 - 逻辑形式对标数据，在没有平行语料的情况下完成跨语言的语义解析，模型加强了语言无关编码，显著优于传统基于翻译的算法，有时能与有监督模型的上线媲美。