Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead

We Text-to-SQL is an important research topic in semantic parsing adapt the two reranking methods from [16], query plan (QP) and [1, 2, 3, 4, 5, 6, 7]. Spider [3] and CoSQL [5] datasets allow for schema linking (SL), and show that both methods can help improve making progress in complex, cross-domain, single and multi-turn multi-turn text-to-SQL. With accuracy on CoSQL being reported text-to-SQL tasks respectively, utilizing a common set of databases, using exact-set-match accuracy (EM) and execution accuracy (EX), with competitive leaderboards, demonstrating the difficulty in the with T5-Large we observed: a) MT leads to 2.4% and 1.7% absolute tasks. In contrast to Spider, CoSQL was collected as entire dialogues, improvement on EM and EX; b) combined reranking approaches and hence includes additional challenges for the text-to-SQL yield 1.9% and 2.2% improvements; c) combining MT with reranking, task in terms of integrating dialogue context. In addition to the with T5-Large we obtain improvements of 2.1% in EM and challenges in general-purpose code generation [8, 9], where the 3.7% in EX over a T5-Large PICARD baseline. This improvement output of the system is constrained to follow a grammar, the textto-SQL is consistent on larger models, using T5-3B yielded about 1.0% in problem is underspecified without a schema.