  Domain-general semantic parsing is a long-standing goal in natural language
processing, where the semantic parser is capable of robustly parsing sentences
from domains outside of which it was trained. Current approaches largely rely
on additional supervision from new domains in order to generalize to those
domains. We present a generative model of natural language utterances and
logical forms and demonstrate its application to semantic parsing. Our approach
relies on domain-independent supervision to generalize to new domains. We
derive and implement efficient algorithms for training, parsing, and sentence
generation. The work relies on a novel application of hierarchical Dirichlet
processes (HDPs) for structured prediction, which we also present in this
manuscript.
  This manuscript is an excerpt of chapter 4 from the Ph.D. thesis of Saparov
(2022), where the model plays a central role in a larger natural language
understanding system.
  This manuscript provides a new simplified and more complete presentation of
the work first introduced in Saparov, Saraswat, and Mitchell (2017). The
description and proofs of correctness of the training algorithm, parsing
algorithm, and sentence generation algorithm are much simplified in this new
presentation. We also describe the novel application of hierarchical Dirichlet
processes for structured prediction. In addition, we extend the earlier work
with a new model of word morphology, which utilizes the comprehensive
morphological data from Wiktionary.
