Papers with DB
An efficient method for Natural Language Querying on Structured Data (2023.acl-industry)
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| Challenge: | a new approach to NLQ on structured data is based on text-to-SQL type semantic parsing . domain classification, domain classification and domain classification are the main tasks . semantic parsed queries are less common when information is in structured form . |
| Approach: | They propose an efficient and reliable approach to natural language Querying on databases . they use domain classification, domain classification and slot/entity extraction to query a DB . |
| Outcome: | The proposed approach simplifies the NLQ on structured data problem to the following "bread and butter" tasks. |
Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information (2025.emnlp-industry)
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| Challenge: | Existing language agents powered by large language models face resource-constrained environments . proprietary models raise concerns in cost and service dependency, while large-scale open-source models require substantial computational resources. |
| Approach: | They propose a Korean chemical toxicity information agent that reduces token consumption . they propose 'scenario-based dialogue generation' methodology that distills tool-using capabilities from larger models. |
| Outcome: | The proposed language agent outperforms untuned models and baseline approaches in DB faithfulness and preference. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
Unsupervised Paraphrasing with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Paraphrase generation has benefited from recent advances in the design of training objectives and model architectures, but previous studies focused on supervised methods that require a large amount of labeled data that is costly to collect. |
| Approach: | They propose a transfer learning approach that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. |
| Outcome: | The proposed model performs state-of-the-art on the Quora Question Pair and ParaNMT datasets and is robust to domain shift between the two datasets. |
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing (2020.findings-emnlp)
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| Challenge: | BRIDGE is a powerful sequential architecture for cross-modal semantic parsing . BRidege captures cross-modal dependencies between natural language questions and relational databases . |
| Approach: | They propose a sequential architecture that captures cross-modal dependencies between questions and relational databases in cross-DB semantic parsing. |
| Outcome: | The proposed architecture performs well on the well-studied Spider benchmark (65.5% dev, 59.2% test). |
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing (P19-1)
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| Challenge: | Semantic parsing to SQL has largely ignored the structure of the database schema . a recent study used a simple DB that was observed at both training and test time. |
| Approach: | They propose a semantic parser where the schema structure is encoded with a graph neural network and used at both encoding and decoding time. |
| Outcome: | The proposed parser improves from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%. |
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database (2022.emnlp-main)
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| Challenge: | Existing approaches on semantic parsing suffer from exponential growth of logical form candidates and can hardly generalize to unseen data. |
| Approach: | They propose a unified semantic parser for question answering on KB and DB . they define the primitive as the essential element in their framework . |
| Outcome: | The proposed framework can predict logical forms by altering and composing top-ranked primitives with different operations. |
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction (2025.acl-long)
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| Challenge: | Multiple-choice questions (MCQs) are critical for identifying misconceptions and gaps in knowledge and accurately assessing students' understanding. |
| Approach: | They propose to train a model to generate distractors that are more likely to be selected by students by a pairwise ranker and a distractor generator via Direct Preference Optimization. |
| Outcome: | The proposed model outperforms baseline models and performs comparable to humans in various metrics including pairwise rank accuracy and distractor plausibility. |
GRAD: Generalizing RAG Adaptation with Decoding (2026.acl-long)
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| Challenge: | Using GRAD, we can steer Retrieval-augmented generation objectives without retraining large language models. |
| Approach: | They propose an adaptive decoding-time framework that keeps the base generator fixed and composes small, objective-specific guidance at inference. |
| Outcome: | The proposed framework improves accuracy with favorable latency across public benchmarks and private settings with no in-domain labels while reliably activating helpful objectives and suppressing harmful ones, adaptively to tasks. |