Summary of Schema-aware Multi-task Learning For Complex Text-to-sql, by Yangjun Wu and Han Wang
Schema-Aware Multi-Task Learning for Complex Text-to-SQL
by Yangjun Wu, Han Wang
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a new framework called MTSQL for generating complex SQL queries from natural language questions. The conventional text-to-SQL parsers struggle with synthesizing multi-table or column queries due to schema item identification and alignment challenges. To address this, the proposed MTSQL framework incorporates a schema linking discriminator module to guide the encoder in aligning question and schema items accurately. On the decoder side, it defines 6-type relationships for describing table-column connections (e.g., WHERE_TC) and an operator-centric triple extractor to recognize associated schema items with predefined relationships. The paper also establishes a rule set of grammar constraints using predicted triples to filter proper SQL operators and schema items during generation. Experimental results on the Spider benchmark show that MTSQL outperforms baselines, especially in extremely hard scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how computers understand and generate complex SQL queries from natural language questions. Right now, computers struggle with generating these types of queries because they have trouble identifying important details about tables and columns. The researchers created a new system called MTSQL that can better identify these details and use them to generate accurate SQL queries. They tested their system on a challenging dataset and found that it was more effective than other systems in difficult scenarios. |
Keywords
» Artificial intelligence » Alignment » Decoder » Encoder
