train-semantic-equivalence-string-match TEC-0051 · proposedTrain on semantic equivalence, not string match
Replaces the MLE objective with a reward that maximizes expected semantic correctness of the generated regex, measured by a DFA-equivalence oracle plus random and distinguishing test cases.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Replaces the MLE objective with a reward that maximizes expected semantic correctness of the generated regex, measured by a DFA-equivalence oracle plus random and distinguishing test cases. Filed by the drafting stage from SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications (zhong-2018-semregex) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Semantic accuracy against prior state-of-the-art syntax-based models on three public NL-to-regex datasets; the paper reports superiority but the excerpt gives no numbers.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.
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Sources
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