  Recent work on Natural Language Interfaces to Databases (NLIDB) has attracted
considerable attention. NLIDB allow users to search databases using natural
language instead of SQL-like query languages. While saving the users from
having to learn query languages, multi-turn interaction with NLIDB usually
involves multiple queries where contextual information is vital to understand
the users' query intents. In this paper, we address a typical contextual
understanding problem, termed as follow-up query analysis. In spite of its
ubiquity, follow-up query analysis has not been well studied due to two primary
obstacles: the multifarious nature of follow-up query scenarios and the lack of
high-quality datasets. Our work summarizes typical follow-up query scenarios
and provides a new FollowUp dataset with $1000$ query triples on 120 tables.
Moreover, we propose a novel approach FANDA, which takes into account the
structures of queries and employs a ranking model with weakly supervised
max-margin learning. The experimental results on FollowUp demonstrate the
superiority of FANDA over multiple baselines across multiple metrics.
