  Semantic parsing using hierarchical representations has recently been
proposed for task oriented dialog with promising results [Gupta et al 2018]. In
this paper, we present three different improvements to the model:
contextualized embeddings, ensembling, and pairwise re-ranking based on a
language model. We taxonomize the errors possible for the hierarchical
representation, such as wrong top intent, missing spans or split spans, and
show that the three approaches correct different kinds of errors. The best
model combines the three techniques and gives 6.4% better exact match accuracy
than the state-of-the-art, with an error reduction of 33%, resulting in a new
state-of-the-art result on the Task Oriented Parsing (TOP) dataset.
