Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
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| Challenge: | Recent advances in neural network parsers address data sparsity issues by modeling character level information and exploiting raw data in semi-supervised settings. |
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Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (2022.findings-acl)
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| Challenge: | Recent work in task-independent graph semantic parsing has shifted from symbolic approaches to neural models, showing strong performance on different types of meaning representations. |
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| Challenge: | Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. |
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Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
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Photon: A Robust Cross-Domain Text-to-SQL System (2020.acl-demos)
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Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, Irwin King
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| Challenge: | In many natural language processing tasks the same input can have multiple possible outputs. |
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Understanding Neural Abstractive Summarization Models via Uncertainty (2020.emnlp-main)
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| Challenge: | Recent advances in abstractive summarization have been fueled by the advent of large-scale Transformers pre-trained on autoregressive language modeling objectives. |
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PARSQL: Enhancing Text-to-SQL through SQL Parsing and Reasoning (2025.findings-acl)
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| Challenge: | Large language models have made significant strides in text-to-SQL tasks, but small language models struggle to accurately interpret natural language questions due to resource limitations. |
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