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Structural Automata for Neural Semantic Parsing

Paper recorded by Signals 4 on 2026-10-09 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.CL · 自然语言处理 · first seen 2026-10-12

Abstract

Neural semantic parsers generate meaning representations token by token, yet well-formedness depends on references, scope, and graph relations that unfold during generation. Constrained decoding prevents locally invalid tokens but does not guarantee that a valid prefix can be completed within a fixed output-length limit. We investigate structural automata at three stages of DRS parsing (decoding,

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#267 most recent of 660 cs.CL papers we have recorded · ↑ newer: When VLMs Destroy What They Know: Spatial Representations Fall Below L · ↓ older: OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment
Cite this page: Structural Automata for Neural Semantic Parsing: the #267 most recent of 660 cs.CL papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/structural-automata-for-neural-semantic-parsing.html
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