# Broad-Coverage Semantic Parsing as Transduction

arXiv: https://arxiv.org/abs/1909.02607 · HTML: https://mlsanity.com/paper/1909.02607/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 30 of 32)

- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Calibrated Interpretation: Confidence Estimation in Semantic Parsing](https://mlsanity.com/paper/2211.07443/) (2022)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [AMR Parsing with Action-Pointer Transformer](https://mlsanity.com/paper/2104.14674/) (2021)
- [Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing](https://mlsanity.com/paper/2110.15534/) (2021)
- [Zero and Few-shot Semantic Parsing with Ambiguous Inputs](https://mlsanity.com/paper/2306.00824/) (2023)
- [Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images](https://mlsanity.com/paper/2110.00519/) (2021)
- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Inducing and Using Alignments for Transition-based AMR Parsing](https://mlsanity.com/paper/2205.01464/) (2022)
- [ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation](https://mlsanity.com/paper/2305.16585/) (2023)
- [Hierarchical Curriculum Learning for AMR Parsing](https://mlsanity.com/paper/2110.07855/) (2021)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (2021)
- [Few-Shot Semantic Parsing for New Predicates](https://mlsanity.com/paper/2101.10708/) (2021)
- [A Graph-Based Neural Model for End-to-End Frame Semantic Parsing](https://mlsanity.com/paper/2109.12319/) (2021)
- [HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction](https://mlsanity.com/paper/2106.15838/) (2021)
- [Joint Universal Syntactic and Semantic Parsing](https://mlsanity.com/paper/2104.05696/) (2021)
- [ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs](https://mlsanity.com/paper/2204.08875/) (2022)
- [Did You Mean...? Confidence-based Trade-offs in Semantic Parsing](https://mlsanity.com/paper/2303.16857/) (2023)
- [One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets](https://mlsanity.com/paper/2106.04476/) (2021)
- [Semantic-based Pre-training for Dialogue Understanding](https://mlsanity.com/paper/2209.09146/) (2022)
- [Levi Graph AMR Parser using Heterogeneous Attention](https://mlsanity.com/paper/2107.04152/) (2021)
- [When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems](https://mlsanity.com/paper/2205.12228/) (2022)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm](https://mlsanity.com/paper/2307.13424/) (2023)
- [A Transition-based Parser for Unscoped Episodic Logical Forms](https://mlsanity.com/paper/2103.08759/) (2021)
- [Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04814/) (2021)
- [Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference](https://mlsanity.com/paper/2204.03619/) (2022)
- [Schema-Free Dependency Parsing via Sequence Generation](https://mlsanity.com/paper/2201.12407/) (2022)
- [Strictly Breadth-First AMR Parsing](https://mlsanity.com/paper/2211.03922/) (2022)

## Abstract (arXiv)

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.