Broad-Coverage Semantic Parsing as Transduction

2019/09/05 by Sheng Zhang, Zhang, Sheng, Xutai Ma +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1909.02607

openalex publication_date 2019/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without relying on a pre-trained aligner. Experiments conducted on three separate broad-coverage semantic parsing tasks -- AMR, SDP and UCCA -- demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.

Citations

• Character-Aware Neural Language Models
• Long Short-Term Memory
• Transition-Based Dependency Parsing with Stack Long Short-Term Memory

Cited by

• Few-Shot Semantic Parsing for New Predicates

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