Structural Automata for Neural Semantic Parsing
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, post hoc processing, and training) and additionally evaluate decoding on AMR. For decoding, we introduce accepting-path decoding (APD), which maintains a verified completion within the remaining length limit and follows the model's preferred token whenever that token can also be completed. APD gu...
| Authors | Xiao Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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