Abstract
By grounding natural language inference in code (and vice versa), researchers aim to create programming assistants that explain their work, are “coachable” and can surface any gaps in their reasoning. Can we deduce automatically interesting properties of programs from their syntax and common-sense annotations alone, without resorting to static analysis? How much of program logic and behaviour can be captured in natural language? To stimulate research in this direction and attempt to answer these questions we propose HTL, a dataset and protocol for annotating programs with natural language predicates at a finer granularity than code comments and without relying on internal compiler representations. The dataset is available at the following address: https://doi.org/10.5281/zenodo.7893113 .- Anthology ID:
- 2023.findings-emnlp.601
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8961–8966
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.601
- DOI:
- 10.18653/v1/2023.findings-emnlp.601
- Cite (ACL):
- Marco Zocca. 2023. Natural Language Annotations for Reasoning about Program Semantics. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8961–8966, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Natural Language Annotations for Reasoning about Program Semantics (Zocca, Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.findings-emnlp.601.pdf