  Semantic parsing (SP) is a core component of modern virtual assistants like
Google Assistant and Amazon Alexa. While sequence-to-sequence-based
auto-regressive (AR) approaches are common for conversational semantic parsing,
recent studies employ non-autoregressive (NAR) decoders and reduce inference
latency while maintaining competitive parsing quality. However, a major
drawback of NAR decoders is the difficulty of generating top-k (i.e., k-best)
outputs with approaches such as beam search. To address this challenge, we
propose a novel NAR semantic parser that introduces intent conditioning on the
decoder. Inspired by the traditional intent and slot tagging parsers, we
decouple the top-level intent prediction from the rest of a parse. As the
top-level intent largely governs the syntax and semantics of a parse, the
intent conditioning allows the model to better control beam search and improves
the quality and diversity of top-k outputs. We introduce a hybrid
teacher-forcing approach to avoid training and inference mismatch. We evaluate
the proposed NAR on conversational SP datasets, TOP & TOPv2. Like the existing
NAR models, we maintain the O(1) decoding time complexity while generating more
diverse outputs and improving the top-3 exact match (EM) by 2.4 points. In
comparison with AR models, our model speeds up beam search inference by 6.7
times on CPU with competitive top-k EM.
