  Conversational text-to-SQL aims at converting multi-turn natural language
queries into their corresponding SQL (Structured Query Language)
representations. One of the most intractable problems of conversational
text-to-SQL is modelling the semantics of multi-turn queries and gathering the
proper information required for the current query. This paper shows that
explicitly modelling the semantic changes by adding each turn and the
summarization of the whole context can bring better performance on converting
conversational queries into SQLs. In particular, we propose two conversational
modelling tasks in both turn grain and conversation grain. These two tasks
simply work as auxiliary training tasks to help with multi-turn conversational
semantic parsing. We conducted empirical studies and achieved new
state-of-the-art results on the large-scale open-domain conversational
text-to-SQL dataset. The results demonstrate that the proposed mechanism
significantly improves the performance of multi-turn semantic parsing.
