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Semantic parsing has long been a fundamental problem in natural languageprocessing. Recently, cross-domain context-dependent semantic parsing hasbecome a new focus of research. Central to the problem is the challenge ofleveraging contextual information of both natural language utterance anddatabase schemas in the interaction history. In this paper, we present adynamic graph framework that is capable of effectively modelling contextualutterances, tokens, database schemas, and their complicated interaction as theconversation proceeds. The framework employs a dynamic memory decay mechanismthat incorporates inductive bias to integrate enriched contextual relationrepresentation, which is further enhanced with a powerful reranking model. Atthe time of writing, we demonstrate that the proposed framework outperforms allexisting models by large margins, achieving new state-of-the-art performance ontwo large-scale benchmarks, the SParC and CoSQL datasets. Specifically, themodel attains a 55.8% question-match and 30.8% interaction-match accuracy onSParC, and a 46.8% question-match and 17.0% interaction-match accuracy onCoSQL.

Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing