As a mainstream approach, grammar-based models have achieved high performance in text-to-sql parsing task, but suffer from low decoding efficiency since the number of actions for building sql trees are much larger tha...
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ISBN:
(纸本)9783031171895;9783031171888
As a mainstream approach, grammar-based models have achieved high performance in text-to-sql parsing task, but suffer from low decoding efficiency since the number of actions for building sql trees are much larger than the number of tokens in sql queries. Meanwhile, intuitively it is beneficial from the parsing performance perspective to incorporate alignment information between sql clauses and question segments. This paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATsql and LGEsql. Experiments on the Spider dataset show our approach improves the decoding speed of RATsql and LGEsql by 18.9% and 35.5% respectively, and also achieves consistent improvement in parsing accuracy, especially on complex questions.
The importance of building text-to-sql parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unsee...
详细信息
ISBN:
(纸本)9781450393850
The importance of building text-to-sql parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating sqls. In this work, we propose a novel framework to elicit relational structures from large-scale pre-trained language models (PLMs) via a probing procedure based on Poincare distance metric, and use the induced relations to augment current graph-based parsers for better schema linking. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences, even when surface forms of mentions and entities differ. Moreover, our probing procedure is entirely unsupervised and requires no additional parameters. Extensive experiments show that our framework sets new state-of-the-art performance on three benchmarks. We empirically verify that our probing procedure can indeed find desired relational structures through qualitative analysis.
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