Abstract
Semantic parsing over multiple knowledge
bases enables a parser to exploit structural similarities of programs across the multiple domains. However, the fundamental challenge
lies in obtaining high-quality annotations of
(utterance, program) pairs across various domains needed for training such models. To
overcome this, we propose a novel framework
to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations). Weakly supervised training
is particularly arduous as the program search
space grows exponentially in a multi-domain
setting. To solve this, we incorporate a multipolicy distillation mechanism in which we first
train domain-specific semantic parsers (teachers) using weak supervision in the absence of
the ground truth programs, followed by training a single unified parser (student) from the
domain specific policies obtained from these
teachers. The resultant semantic parser is not
only compact but also generalizes better, and
generates more accurate programs. It further does not require the user to provide a
domain label while querying. On the standard OVERNIGHT dataset (containing multiple
domains), we demonstrate that the proposed
model improves performance by 20% in terms
of denotation accuracy in comparison to baseline techniques.