Papers by Mingwen Dong
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)
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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang
| Challenge: | Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data. |
| Approach: | They propose a framework that imposes strong typing constraints and incorporates key relationships from schema. |
| Outcome: | The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider. |
Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods struggle to effectively propagate updates to interconnected facts, limiting the performance of reasoning tasks based on these updated facts. |
| Approach: | They propose a reasoning-based benchmark, ReCoE, which covers six common reasoning schemes in the real world. |
| Outcome: | The proposed reasoning-based benchmark shows that current models struggle to propagate updated knowledge within reasoning schemes. |
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries (2025.naacl-long)
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Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu, Anuj Chauhan, Chung-Wei Hang, Shuaichen Chang, Lin Pan, Wuwei Lan, Henghui Zhu, Jiarong Jiang, Patrick Ng, Zhiguo Wang
| Challenge: | Existing text-to-SQL systems focus on user questions with clear intentions that can be answered, but real user questions can be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. |
| Approach: | They construct a conversational text-to-SQL dataset called PRACTIQ, consisting of ambiguous and unanswerable questions inspired by real-world user questions. |
| Outcome: | The proposed system generates conversations with four turns, generating the user’s question, an assistant response seeking clarification, and the user's clarified SQL response with the natural language explanation of the execution results. |