| Challenge: | Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping. |
| Approach: | They propose to use a Chinese-based semantic parser to map natural language text into SQL. |
| Outcome: | The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping. |
Similar Papers
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing (2020.acl-main)
Copied to clipboard
| Challenge: | Existing evaluation datasets such as Spider are used to support cross-database semantic parsing . XSP systems that map natural language utterances to SQL queries are evaluated on databases unseen during training. |
| Approach: | They propose a setup that uses eight well-studied datasets to evaluate cross-database semantic parsing systems. |
| Outcome: | The proposed system performs well on spider, but struggles to generalize to the repurposed set. |
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
Copied to clipboard
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
| Approach: | They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries . |
| Outcome: | The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting. |
Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers. |
| Approach: | They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task. |
| Outcome: | The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance. |
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches generate question reformulations via models trained on Spider or only introduce local changes. Existing methods generate question form reformulation but lack robustness. |
| Approach: | They use data augmentation to enhance the robustness of text-to-SQL parsers against natural language variations by generating more realistic and diverse questions. |
| Outcome: | The proposed model improves on the new spider dataset by using a few prompts. |
Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)
Copied to clipboard
| Challenge: | Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words . |
| Approach: | They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks. |
| Outcome: | The proposed model outperforms char-based models in four NLP benchmark tasks. |
Global Reasoning over Database Structures for Text-to-SQL Parsing (D19-1)
Copied to clipboard
| Challenge: | Existing semantic parsers only select a set of database constants at training time . current models only consider local information, not global ones . |
| Approach: | They propose a semantic parser that globally reasons about the structure of the query to make a more contextually-informed selection of database constants. |
| Outcome: | The proposed model increases accuracy from 39.4% to 47.4% on a zero-shot semantic parsing dataset with complex databases. |
Beyond Read-Only: Crafting a Comprehensive Chinese Text-to-SQL Dataset for Database Manipulation and Query (2024.findings-naacl)
Copied to clipboard
| Challenge: | Current research focuses mainly on read operations and ignores other aspects of database operations such as create, update, and delete operations. |
| Approach: | They propose a large-scale cross-domain single-table CRUD operations Chinese Text-to-SQL dataset . the dataset contains 10,000 question/SQl pairs involving 625 tables from different domains . |
| Outcome: | The proposed method achieves 67.08% and 83.8% exact set matching accuracy under read and delete operations, but only 49.6% and 61.8% under create and update operations. |
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task . |
| Approach: | They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework . |
| Outcome: | The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs. |
PARSQL: Enhancing Text-to-SQL through SQL Parsing and Reasoning (2025.findings-acl)
Copied to clipboard
| 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. |
| Approach: | They propose a SQL parser that extracts constraints from SQL to generate sub-SQLs . they use a rule-based and LLM-based method to generate step-by-step SQL explanations based on the results . |
| Outcome: | The proposed framework outperforms models with the same model size on BIRD and Spider benchmarks. |
PAUQ: Text-to-SQL in Russian (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Semantic parsing is an important task that allows to democratize human-computer interaction. |
| Approach: | They construct and complement a Russian text-to-SQL dataset by integrating a spider query with a RAT-SqL and BRIDGE database. |
| Outcome: | The proposed datasets show that they perform well with monolingual training and improved accuracy in multilingual scenarios. |