论文标题
RASAT：将关系结构集成到文本到SQL的验证的SEQ2SEQ模型中
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
论文作者
论文摘要
诸如模式链接和模式编码之类的关系结构已被验证为将自然语言定性转化为SQL查询的关键组成部分。但是，引入这些结构关系伴随着价格：它们通常会导致专门的模型结构，该结构在很大程度上禁止在文本到SQL中使用大型预位模型。为了解决这个问题，我们提出了RASAT：一种变压器SEQ2SEQ结构增强，并以意识到的自我发明，可以利用各种关系结构，同时有效地继承了T5模型的预告片。我们的模型几乎可以纳入文献中的几乎所有类型的现有关系，此外，我们提出了针对多转变场景的共同参考关系。涵盖单转弯和多转弯情况的三个广泛使用的文本到SQL数据集的实验结果表明，RASAT可以在所有三个基准测试中实现最新的结果（Spider上的75.5％，SPARC上的52.6％IEX和37.4％的IEX IEX在COSQL上）。
Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries. However, introducing these structural relations comes with prices: they often result in a specialized model structure, which largely prohibits using large pretrained models in text-to-SQL. To address this problem, we propose RASAT: a Transformer seq2seq architecture augmented with relation-aware self-attention that could leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model effectively. Our model can incorporate almost all types of existing relations in the literature, and in addition, we propose introducing co-reference relations for the multi-turn scenario. Experimental results on three widely used text-to-SQL datasets, covering both single-turn and multi-turn scenarios, have shown that RASAT could achieve state-of-the-art results across all three benchmarks (75.5% EX on Spider, 52.6% IEX on SParC, and 37.4% IEX on CoSQL).
