  Translating natural language queries into SQLs in a seq2seq manner has
attracted much attention recently. However, compared with
abstract-syntactic-tree-based SQL generation, seq2seq semantic parsers face
much more challenges, including poor quality on schematical information
prediction and poor semantic coherence between natural language queries and
SQLs. This paper analyses the above difficulties and proposes a
seq2seq-oriented decoding strategy called SR, which includes a new intermediate
representation SSQL and a reranking method with score re-estimator to solve the
above obstacles respectively. Experimental results demonstrate the
effectiveness of our proposed techniques and T5-SR-3b achieves new
state-of-the-art results on the Spider dataset.
