CoChat
Sign Up
Cited by 216Natural Language Processing TechniquesTopic ModelingSpeech and dialogue systems

The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.

A Graph-to-Sequence Model for AMR-to-Text Generation

Open access
Song, Linfeng, Zhang, Yue, Wang, Zhiguo, Gildea, Daniel·2018·Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/p18-1150
Open this paper in CoChat — free accountOpen PDF

Abstract

The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure.Although it is able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences.We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics.On a standard benchmark, our model shows superior results to existing methods in the literature.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/p18-1150

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 36 references

Research works printed but not linked: 3

  1. Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Simaan. 2017. Graph convolutional encoders for syntax-aware neural ma- chine translation. In Conference on Empirical Meth- ods in Natural Language Processing (EMNLP-17), pages 1957-1967, Copenhagen, Denmark.
  2. Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2017. AMR-to-text gen- eration with synchronous node replacement gram- mar. In Proceedings of the 55th Annual Meet- ing of the Association for Computational Linguistics (ACL-17), pages 7-13, Vancouver, Canada.
  3. Aleš Tamchyna, Chris Quirk, and Michel Galley. 2015. A discriminative model for semantics- to-string translation. In Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015), pages 30-36, Beijing, China.

Cited by

Sorted by citations

Items 201–215 of 215

Related

See all

Metadata

Authors
Song, L., Zhang, Y., Wang, Z., & Gildea, D.
Affiliations
  • University of Rochester (US)
  • Singapore University of Technology and Design (SG)
  • IBM (United States) (US)
ORCID
Venue
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2018
Pages
1616-1626
ISSN
0736-587X (ISSN-L)
DOI
10.18653/v1/p18-1150
OpenAlex
W2963374482
Cited by
216
References
36
Citations by year
2026: 2 · 2025: 6 · 2024: 8 · 2023: 27 · 2022: 23 · 2021: 64 · 2020: 44 · 2019: 34 · 2018: 8
FWCI
15.31
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • University of Rochester
  • Centre International de Recherche sur le Cancer
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
AMR-to-text generation, Abstract Meaning Representation, graph neural networks, LSTM, sequence-to-sequence models

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review