  Neural semantic parsers usually fail to parse long and complex utterances
into correct meaning representations, due to the lack of exploiting the
principle of compositionality. To address this issue, we present a novel
framework for boosting neural semantic parsers via iterative utterance
segmentation. Given an input utterance, our framework iterates between two
neural modules: a segmenter for segmenting a span from the utterance, and a
parser for mapping the span into a partial meaning representation. Then, these
intermediate parsing results are composed into the final meaning
representation. One key advantage is that this framework does not require any
handcraft templates or additional labeled data for utterance segmentation: we
achieve this through proposing a novel training method, in which the parser
provides pseudo supervision for the segmenter. Experiments on Geo,
ComplexWebQuestions, and Formulas show that our framework can consistently
improve performances of neural semantic parsers in different domains. On data
splits that require compositional generalization, our framework brings
significant accuracy gains: Geo 63.1 to 81.2, Formulas 59.7 to 72.7,
ComplexWebQuestions 27.1 to 56.3.
