Shujie Li
2024
TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information
Ziqiang Liu
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Shujie Li
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Zefeng Cai
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Xiangyu Li
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Yunshui Li
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Chengming Li
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Xiping Hu
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Ruifeng Xu
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Min Yang
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
In this paper, we introduce an innovative pre-training framework TP-Link, which aims to improve context-dependent Text-to-SQL Parsing by leveraging Linking information. This enhancement is achieved through better representation of both natural language utterances and the database schema, ultimately facilitating more effective text-to-SQL conversations. We present two novel pre-training objectives: (i) utterance linking prediction (ULP) task that models intricate syntactic relationships among natural language utterances in context-dependent text-to-SQL scenarios, and (ii) schema linking prediction (SLP) task that focuses on capturing fine-grained schema linking relationships between the utterances and the database schema. Extensive experiments demonstrate that our proposed TP-Link achieves state-of-the-art performance on two leading downstream benchmarks (i.e., SParC and CoSQL).
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Co-authors
- Ziqiang Liu 1
- Zefeng Cai 1
- Xiangyu Li 1
- Yunshui Li 1
- Chengming Li 1
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