  Semantic parsing has long been a fundamental problem in natural language
processing. Recently, cross-domain context-dependent semantic parsing has
become a new focus of research. Central to the problem is the challenge of
leveraging contextual information of both natural language utterance and
database schemas in the interaction history. In this paper, we present a
dynamic graph framework that is capable of effectively modelling contextual
utterances, tokens, database schemas, and their complicated interaction as the
conversation proceeds. The framework employs a dynamic memory decay mechanism
that incorporates inductive bias to integrate enriched contextual relation
representation, which is further enhanced with a powerful reranking model. At
the time of writing, we demonstrate that the proposed framework outperforms all
existing models by large margins, achieving new state-of-the-art performance on
two large-scale benchmarks, the SParC and CoSQL datasets. Specifically, the
model attains a 55.8% question-match and 30.8% interaction-match accuracy on
SParC, and a 46.8% question-match and 17.0% interaction-match accuracy on
CoSQL.
