---
title: "AMR Parsing via Graph-Sequence Iterative — Cai 2020"
description: "The paper addresses AMR parsing, which converts a sentence into a rooted, directed, labeled semantic graph."
authors: ["Deng Cai", "Wai Lam"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.119/"
doi: "10.18653/v1/2020.acl-main.119"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cai-2020-amr-parsing-graph-sequence-iterative/"
---
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# AMR Parsing via Graph-Sequence Iterative Inference

**Authors:** [Deng Cai](https://md.research.elamur.ai/free/en/authors/deng-cai/), [Wai Lam](https://md.research.elamur.ai/free/en/authors/wai-lam/) · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.119/) · [PDF](https://aclanthology.org/2020.acl-main.119.pdf) · [DOI 10.18653/v1/2020.acl-main.119](https://doi.org/10.18653/v1/2020.acl-main.119) · [OpenAlex](https://openalex.org/W3035586188)

**Citations:** 89 citations (openalex-snapshot · 2026-10-14) · [79](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses AMR parsing, which converts a sentence into a rooted, directed, labeled semantic graph. A central difficulty is that graph concepts have no explicit alignment to words, so deciding which concept to add and how to connect it are closely linked. Cai and Lam build the graph one node at a time. Each expansion step is treated as two coupled decisions: which span of the sentence to abstract into a new concept, and where in the partial graph to attach it. The model uses a Transformer sequence encoder, an incremental graph encoder, and two attention-based solvers, one for concepts and one for relations. These solvers repeatedly refine each other's hypotheses over several reasoning rounds before the graph is expanded. The parser needs no pre-trained aligner. On LDC2017T10 (AMR 2.0) and LDC2014T12 (AMR 1.0), it sets new state-of-the-art SMATCH scores. Even without BERT, it beats earlier BERT-based systems. This suggests that explicitly modeling how concept and relation choices interact is a data-efficient route to more accurate semantic parsing.

## Key points
- **Best SMATCH (F1) with BERT:** 80.2% on AMR 2.0 (LDC2017T10) and 75.4% on AMR 1.0 (LDC2014T12). The version without BERT already beats the earlier state of the art that used BERT.
- **Fine-grained evaluation (AMR 2.0, Damonte et al. tool):** gains of about 2–3% across nearly all sub-tasks over Zhang et al. (2019b). NER scores fall when graph re-categorization is used, because its rule-based entity handling generalizes poorly.
- **Number of inference steps:**
  - The largest gain comes from moving from 1 to 2 steps.
  - Longer sentences benefit most from extra steps.
  - The parameter count does not change with the number of steps.
- **Beam size:** a beam of 2 captures most of the achievable improvement.
- **Efficiency:** complexity is O(kbn), which is linear in graph size. Unoptimized code parses about 300 tokens/s with BERT and about 330 tokens/s without it on one Nvidia P4 GPU.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) · [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

79 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 70 not yet.

- [Graph Pre-training for AMR Parsing and Generation](https://md.research.elamur.ai/free/en/papers/bai-2022-graph-pre-training-amr-parsing/)
- [Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations](https://md.research.elamur.ai/free/en/papers/huang-2022-unsupervised-syntactically-controlled-paraphrase-generation/)
- [ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation](https://md.research.elamur.ai/free/en/papers/huang-2023-paraamr-large-scale-syntactically-diverse/)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://md.research.elamur.ai/free/en/papers/jangra-2022-t-star-truthful-style-transfer/)
- [Referenceless Parsing-Based Evaluation of AMR-to-English Generation](https://md.research.elamur.ai/free/en/papers/manning-2021-referenceless-parsing-based-evaluation-amr/)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
- [Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR](https://md.research.elamur.ai/free/en/papers/opitz-2021-decomposable-metric-explainable-evaluation-text/)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/)
- [A Double-Graph Based Framework for Frame Semantic Parsing](https://md.research.elamur.ai/free/en/papers/zheng-2022-double-graph-based-framework-frame/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{cai2020amr,
  title = {{AMR Parsing via Graph-Sequence Iterative Inference}},
  author = {Cai, Deng and Lam, Wai},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.119},
  url = {https://doi.org/10.18653/v1/2020.acl-main.119}
}
```

- [← previous AMALGUM – A Free, Balanced, Multilayer English Web Corpus](https://md.research.elamur.ai/free/en/papers/lingbulk-2020-gessler-amalgum-free-balanced-multilayer-english/)
- [next → AMR-To-Text Generation with Graph Transformer](https://md.research.elamur.ai/free/en/papers/wang-2020-amr-text-generation-graph-transformer/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/)

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37 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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