The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Learning Dependency-Based Compositional Semantics
Open accessAbstract
Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive. Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.
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Self-citationNatural language interfaces to databases – an introduction
Androutsopoulos, I., Ritchie, G. D., & Thanisch, P. (1995) · Natural Language Engineering · doi:10.1017/s135132490000005x · Cited by 768
Cited by
A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P., & Lillicrap, T. (2017) · arXiv · doi:10.48550/arxiv.1706.01427 · Cited by 1,281
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Zhong, V., Xiong, C., & Socher, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00103 · Cited by 733
Language to Logical Form with Neural Attention
Dong, L., & Lapata, M. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1004 · Cited by 700
Metadata
- Authors
- Liang, P., Jordan, M. I., & Klein, D.
- Venue
- Computational Linguistics
- Publisher
- MIT Press
- Published
- 2013-06
- Volume
- 39
- Issue
- 2
- Pages
- 389-446
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2161002933
- Cited by
- 546
- References
- 5
- FWCI
- 52.56
- Type
- journal article
- Language
- en
- Open access
- green
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