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Context-dependent semantic parsing has proven to be an important yetchallenging task. To leverage the advances in context-independent semanticparsing, we propose to perform follow-up query analysis, aiming to restatecontext-dependent natural language queries with contextual information. Toaccomplish the task, we propose STAR, a novel approach with a well-designedtwo-phase process. It is parser-independent and able to handle multifariousfollow-up scenarios in different domains. Experiments on the FollowUp datasetshow that STAR outperforms the state-of-the-art baseline by a large margin ofnearly 8%. The superiority on parsing results verifies the feasibility offollow-up query analysis. We also explore the extensibility of STAR on the SQAdataset, which is very promising.

A Split-and-Recombine Approach for Follow-up Query Analysis