Fast semantic parsing with well-typedness guarantees
Matthias Lindemann, Jonas Groschwitz, Alexander Koller
2020年份
2被引次数
4顶会引用
摘要
AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.
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引用它的顶会 Paper4
- A Differentiable Relaxation of Graph Segmentation and Alignment for AMR ParsingChunchuan Lyu, Shay B. Cohen, Ivan TitovEMNLP 2021 · 被引用 13 次
- AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation SuiteJonas Groschwitz, Shay B. Cohen, Lucia Donatelli, Meaghan FowlieEMNLP 2023 · 被引用 5 次
- SLOG: A Structural Generalization Benchmark for Semantic ParsingBingzhi Li, Lucia Donatelli, Alexander Koller, Tal Linzen 等EMNLP 2023 · 被引用 3 次
- From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic DecodingShan Wu, Bo Chen, Chunlei Xin, Xianpei Han 等ACL 2021
它引用的顶会 Paper2
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