  Semantic parsing using sequence-to-sequence models allows parsing of deeper
representations compared to traditional word tagging based models. In spite of
these advantages, widespread adoption of these models for real-time
conversational use cases has been stymied by higher compute requirements and
thus higher latency. In this work, we propose a non-autoregressive approach to
predict semantic parse trees with an efficient seq2seq model architecture. By
combining non-autoregressive prediction with convolutional neural networks, we
achieve significant latency gains and parameter size reduction compared to
traditional RNN models. Our novel architecture achieves up to an 81% reduction
in latency on TOP dataset and retains competitive performance to non-pretrained
models on three different semantic parsing datasets. Our code is available at
https://github.com/facebookresearch/pytext
