Data Recombination for Neural Semantic Parsing
Open accessAbstract
Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results.In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model.From the training data, we induce a highprecision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing.We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sampled from this grammar, thereby teaching the model about these structural properties.Data recombination improves the accuracy of our RNN model on three semantic parsing datasets, leading to new state-of-the-art performance on the standard GeoQuery dataset for models with comparable supervision.
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For manuscript 726, retrieval supplied explicit comparisons with related semantic-parsing research by Dong and Lapata (2016) and Jia and Liang (2016), which appeared in the final review output.
“For manuscript 726, retrieval supplied explicit comparisons with related semantic-parsing research by Dong and Lapata (2016) and Jia and Liang (2016), which appeared in the final review output.”
From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Jia and Liang (2016)
The paper’s Problem statement (§2) establishes Jia and Liang (2016) as semantic-parsing research, and its References list Dong and Lapata (2016). It does not address manuscript 726, what retrieval supplied, or what appeared in a final review output; those parts of the claim are not supported by this source. (LLM, conf=0.84)
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Ali, Ș., Oprea, S., & Bâra, A. (2026) · Systems Research and Behavioral Science · doi:10.1002/sres.70163
- Authors
- Jia, R., & Liang, P.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 12-22
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2963655793
- Cited by
- 441
- References
- 3
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 14 · 2023: 40 · 2022: 39 · 2021: 87 · 2020: 86 · 2019: 71 · 2018: 53 · 2017: 34 · 2016: 3
- FWCI
- 28.92
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation ((DGE-114747, 114747)
- Defense Advanced Research Projects Agency
- Army Research Office (W911NF-15-1-, W911NF, W911NF-15)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- semantic parsing, conditional independence, recurrent neural networks, prior knowledge incorporation, natural language understanding
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