@inproceedings{qi-etal-2022-rasat,
title = "{RASAT}: Integrating Relational Structures into Pretrained {S}eq2{S}eq Model for Text-to-{SQL}",
author = "Qi, Jiexing and
Tang, Jingyao and
He, Ziwei and
Wan, Xiangpeng and
Cheng, Yu and
Zhou, Chenghu and
Wang, Xinbing and
Zhang, Quanshi and
Lin, Zhouhan",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.211",
doi = "10.18653/v1/2022.emnlp-main.211",
pages = "3215--3229",
abstract = "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 competitive results in all three benchmarks, achieving state-of-the-art execution accuracy (75.5{\%} EX on Spider, 52.6{\%} IEX on SParC, and 37.4{\%} IEX on CoSQL).",
}
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<abstract>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 competitive results in all three benchmarks, achieving state-of-the-art execution accuracy (75.5% EX on Spider, 52.6% IEX on SParC, and 37.4% IEX on CoSQL).</abstract>
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%0 Conference Proceedings
%T RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
%A Qi, Jiexing
%A Tang, Jingyao
%A He, Ziwei
%A Wan, Xiangpeng
%A Cheng, Yu
%A Zhou, Chenghu
%A Wang, Xinbing
%A Zhang, Quanshi
%A Lin, Zhouhan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F qi-etal-2022-rasat
%X 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 competitive results in all three benchmarks, achieving state-of-the-art execution accuracy (75.5% EX on Spider, 52.6% IEX on SParC, and 37.4% IEX on CoSQL).
%R 10.18653/v1/2022.emnlp-main.211
%U https://aclanthology.org/2022.emnlp-main.211
%U https://doi.org/10.18653/v1/2022.emnlp-main.211
%P 3215-3229
Markdown (Informal)
[RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL](https://aclanthology.org/2022.emnlp-main.211) (Qi et al., EMNLP 2022)
ACL
- Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, and Zhouhan Lin. 2022. RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3215–3229, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.