  The task of multi-turn text-to-SQL semantic parsing aims to translate natural
language utterances in an interaction into SQL queries in order to answer them
using a database which normally contains multiple table schemas. Previous
studies on this task usually utilized contextual information to enrich
utterance representations and to further influence the decoding process. While
they ignored to describe and track the interaction states which are determined
by history SQL queries and are related with the intent of current utterance. In
this paper, two kinds of interaction states are defined based on schema items
and SQL keywords separately. A relational graph neural network and a non-linear
layer are designed to update the representations of these two states
respectively. The dynamic schema-state and SQL-state representations are then
utilized to decode the SQL query corresponding to current utterance.
Experimental results on the challenging CoSQL dataset demonstrate the
effectiveness of our proposed method, which achieves better performance than
other published methods on the task leaderboard.
