论文标题
通过子句级的平行解码和对齐损失，基于语法的更快，更好的语法解析
Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss
论文作者
论文摘要
基于语法的解析器在跨域文本到SQL解析任务中实现了高性能，但是由于语法选择的动作数量要比SQL查询中的令牌较大，因此由于语法选择的动作数量要大得多。同时，如何更好地调整SQL条款和问题段是解析性能的关键挑战。因此，本文提出了条款级的平行解码和对齐损失，以增强两个高性能语法基于语法的解析器，即ratsql和lgesql。两个解析器的实验结果表明，我们的方法在准确性和解码速度方面均获得一致的改进。
Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries. Meanwhile, how to better align SQL clauses and question segments has been a key challenge for parsing performance. Therefore, this paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experimental results of two parsers show that our method obtains consistent improvements both in accuracy and decoding speed.
