  Semantic parsing is one of the key components of natural language
understanding systems. A successful parse transforms an input utterance to an
action that is easily understood by the system. Many algorithms have been
proposed to solve this problem, from conventional rulebased or statistical
slot-filling systems to shiftreduce based neural parsers. For complex parsing
tasks, the state-of-the-art method is based on autoregressive sequence to
sequence models to generate the parse directly. This model is slow at inference
time, generating parses in O(n) decoding steps (n is the length of the target
sequence). In addition, we demonstrate that this method performs poorly in
zero-shot cross-lingual transfer learning settings. In this paper, we propose a
non-autoregressive parser which is based on the insertion transformer to
overcome these two issues. Our approach 1) speeds up decoding by 3x while
outperforming the autoregressive model and 2) significantly improves
cross-lingual transfer in the low-resource setting by 37% compared to
autoregressive baseline. We test our approach on three well-known monolingual
datasets: ATIS, SNIPS and TOP. For cross lingual semantic parsing, we use the
MultiATIS++ and the multilingual TOP datasets.
