  Context-dependent semantic parsing has proven to be an important yet
challenging task. To leverage the advances in context-independent semantic
parsing, we propose to perform follow-up query analysis, aiming to restate
context-dependent natural language queries with contextual information. To
accomplish the task, we propose STAR, a novel approach with a well-designed
two-phase process. It is parser-independent and able to handle multifarious
follow-up scenarios in different domains. Experiments on the FollowUp dataset
show that STAR outperforms the state-of-the-art baseline by a large margin of
nearly 8%. The superiority on parsing results verifies the feasibility of
follow-up query analysis. We also explore the extensibility of STAR on the SQA
dataset, which is very promising.
