  Recently, Text-to-SQL for multi-turn dialogue has attracted great interest.
Here, the user input of the current turn is parsed into the corresponding SQL
query of the appropriate database, given all previous dialogue history. Current
approaches mostly employ end-to-end models and consequently face two
challenges. First, dialogue history modeling and Text-to-SQL parsing are
implicitly combined, hence it is hard to carry out interpretable analysis and
obtain targeted improvement. Second, SQL annotation of multi-turn dialogue is
very expensive, leading to training data sparsity. In this paper, we propose a
novel decoupled multi-turn Text-to-SQL framework, where an utterance rewrite
model first explicitly solves completion of dialogue context, and then a
single-turn Text-to-SQL parser follows. A dual learning approach is also
proposed for the utterance rewrite model to address the data sparsity problem.
Compared with end-to-end approaches, the proposed decoupled method can achieve
excellent performance without any annotated in-domain data. With just a few
annotated rewrite cases, the decoupled method outperforms the released
state-of-the-art end-to-end models on both SParC and CoSQL datasets.
