# A Pilot Study for Chinese SQL Semantic Parsing

arXiv: https://arxiv.org/abs/1909.13293 · HTML: https://mlsanity.com/paper/1909.13293/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 30 of 33)

- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
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- [A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions](https://mlsanity.com/paper/2208.13629/) (2022)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2210.13693/) (2022)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2212.13492/) (2022)
- [Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning](https://mlsanity.com/paper/2407.03227/) (2024)
- [Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey](https://mlsanity.com/paper/2208.04415/) (2022)
- [Meta-Learning a Cross-lingual Manifold for Semantic Parsing](https://mlsanity.com/paper/2209.12577/) (2022)
- [A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges](https://mlsanity.com/paper/2412.05208/) (2024)
- [Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges](https://mlsanity.com/paper/2508.00217/) (2025)
- [Multilingual Text-to-SQL: Benchmarking the Limits of Language Models with Collaborative Language Agents](https://mlsanity.com/paper/2509.24405/) (2025)
- [XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations](https://mlsanity.com/paper/2306.04085/) (2023)
- [Automatic Data Visualization Generation from Chinese Natural Language Questions](https://mlsanity.com/paper/2309.07650/) (2023)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities](https://mlsanity.com/paper/2505.23838/) (2025)
- [AnnotatedTables: A Large Tabular Dataset with Language Model Annotations](https://mlsanity.com/paper/2406.16349/) (2024)
- [BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios](https://mlsanity.com/paper/2410.22925/) (2024)
- [Text2Cypher Across Languages: Evaluating and Finetuning LLMs](https://mlsanity.com/paper/2506.21445/) (2025)
- [MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering](https://mlsanity.com/paper/2502.17253/) (2025)
- [Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence](https://mlsanity.com/paper/2507.10281/) (2025)
- [READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises](https://mlsanity.com/paper/2302.07324/) (2023)
- [Beyond SELECT: A Comprehensive Taxonomy-Guided Benchmark for Real-World Text-to-SQL Translation](https://mlsanity.com/paper/2511.13590/) (2025)
- [AraSpider: Democratizing Arabic-to-SQL](https://mlsanity.com/paper/2402.07448/) (2024)
- [Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing](https://mlsanity.com/paper/2410.00513/) (2024)
- [Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems](https://mlsanity.com/paper/2508.14982/) (2025)
- [Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation](https://mlsanity.com/paper/2510.24762/) (2025)

## Abstract (arXiv)

The task of semantic parsing is highly useful for dialogue and question answering systems. Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides cross-domain samples with multiple tables and complex queries. We build a Spider dataset for Chinese, which is currently a low-resource language in this task area. Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English. We compare character- and word-based encoders for a semantic parser, and different embedding schemes. Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.