Papers › T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
Yuntao Li, Zhenpeng Su, Yutian Li, Hanchu Zhang, Sirui Wang, Wei Wu, Yan Zhang
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-To-SQL | spider | T5-SR | Exact Match Accuracy (Dev) | 77.2 | #11 of 20 | Archive leaderboard | report |
| Text-To-SQL | spider | T5-SR | Exact Match Accuracy (Test) | 72.4 | #11 of 20 | Archive leaderboard | report |
| Text-To-SQL | spider | T5-SR | Execution Accuracy (Test) | 75.2 | #11 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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