Language to Logical Form with Neural Attention
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Semantic parsing aims at mapping natural language to machine interpretable meaning representations.Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domainor representation-specific.In this paper we present a general method based on an attention-enhanced encoder-decoder model.We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors.Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.
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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.
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- Authors
- Dong, L., & Lapata, M.
- Affiliations
- University of Edinburgh (GB)
- Corresponding author
- Dong, Li
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 33-43
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2963794306
- Cited by
- 700
- References
- 5
- Citations by year
- 2026: 20 · 2025: 25 · 2024: 39 · 2023: 75 · 2022: 62 · 2021: 115 · 2020: 101 · 2019: 124 · 2018: 83 · 2017: 46 · 2016: 10
- FWCI
- 47.24
- 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)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- H2020 European Research Council (681760)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- semantic parsing, logical form generation, attention mechanism, encoder-decoder architecture, meaning representation, natural language understanding, domain adaptation
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