Natural Language Annotations for Reasoning about Program Semantics
Submission Type: Regular Short Paper
Submission Track: NLP Applications
Submission Track 2: Resources and Evaluation
Keywords: program understanding, natural language reasoning, dataset
TL;DR: We introduce a dataset and annotation protocol for interpretable program understanding.
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 .
Submission Number: 134
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