| Challenge: | Pre-trained parsers perform poorly on domain-specific questions, a paper argues . retraining parser with domain- specific questions is expensive, as these require linguistic expertise. |
| Approach: | They propose an automatic labeled domain question generation framework leveraging domain knowledge and seed domain questions. |
| Outcome: | The proposed framework improves state-of-the-art parsers on domain questions. |
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| Challenge: | Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases. |
| Approach: | They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar. |
| Outcome: | The proposed framework can produce high-quality natural language questions over strong baselines. |
Open-World Factually Consistent Question Generation (2023.findings-acl)
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| Challenge: | Existing methods for question generation suffer from factual inconsistencies and incorrect entities and are not answerable from the input paragraph. |
| Approach: | They propose a data processing technique based on de-lexicalization for consistent question generation across domains and a model that is generic across question-generation models. |
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DSQG-Syn: Synthesizing High-quality Data for Text-to-SQL Parsing by Domain Specific Question Generation (2025.findings-naacl)
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Shaoming Duan, Youxuan Wu, Chuanyi Liu, Yuhao Zhang, Zirui Wang, Peiyi Han, Shengyuan Yu, Liang Yan, Yingwei Liang
| Challenge: | Existing methods for generating SQL queries using natural language questions produce inconsistent NLQ-SQL pairs. |
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Simple Questions Generate Named Entity Recognition Datasets (2022.emnlp-main)
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| Challenge: | Recent named entity recognition models rely on human-annotated datasets . however, in-domain dictionaries and sentences are often unavailable or expensive to construct for many entity types. |
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Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
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Synthetic Question Value Estimation for Domain Adaptation of Question Answering (2022.acl-long)
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| Challenge: | Existing work adapts QA scores to select high-quality questions, but these scores do not improve QA performance on the target domain. |
| Approach: | They propose to synthesize QA pairs with a question generator on the target domain . they propose to train a Question Value Estimator that estimates usefulness of synthetic questions . |
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Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training (2022.findings-naacl)
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| Challenge: | Existing question generation models require large-scale and high-quality training data. |
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Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
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Domain Adaptation for Question Answering via Question Classification (2022.coling-1)
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| Challenge: | Question answering systems often experience performance deterioration upon user-generated questions. |
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Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
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