Specifying and Verbalising Answer Set Programs in Controlled Natural Language
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Abstract (arXiv)
We show how a bi-directional grammar can be used to specify and verbalise answer set programs in controlled natural language. We start from a program specification in controlled natural language and translate this specification automatically into an executable answer set program. The resulting answer set program can be modified following certain naming conventions and the revised version of the program can then be verbalised in the same subset of natural language that was used as specification language. The bi-directional grammar is parametrised for processing and generation, deals with referring expressions, and exploits symmetries in the data structure of the grammar rules whenever these grammar rules need to be duplicated. We demonstrate that verbalisation requires sentence planning in order to aggregate similar structures with the aim to improve the readability of the generated specification. Without modifications, the generated specification is always semantically equivalent to the original one; our bi-directional grammar is the first one that allows for semantic round-tripping in the context of controlled natural language processing. This paper is under consideration for acceptance in TPLP.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2018.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024LLASP: Fine-tuning Large Language Models for Answer Set Programmingcited by 8
- 2024Towards Automatic Composition of ASP Programs from Natural Language Specificationscited by 7
- 2023CNL2ASP: converting controlled natural language sentences into ASPcited by 5
- 2026BLAST: Benchmarking LLMs with ASP-based Structured Testingcited by 0
- 2026BLAST: Benchmarking LLMs with ASP-based Structured Testing24 Apr 2026
- 2024LLASP: Fine-tuning Large Language Models for Answer Set Programming26 Jul 2024
- 2024Towards Automatic Composition of ASP Programs from Natural Language Specifications7 Mar 2024
- 2023CNL2ASP: converting controlled natural language sentences into ASP17 Nov 2023