AMR-to-Text Generation

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (36 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Investigating Pretrained Language Models for Graph-to-Text Generation
  16 Jul 2020 3 repositories listed Syntology ran 2 of 12 samples · 10 unverified
  We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
• Graph Pre-training for AMR Parsing and Generation
  15 Mar 2022 2 repositories listed Syntology ran 3 of 5 samples · 2 unverified
  To our knowledge, we are the first to consider pre-training on semantic graphs.
• Structural Neural Encoders for AMR-to-text Generation
  27 Mar 2019 2 repositories listed
  AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs.
• Generating Text from Uniform Meaning Representation
  17 Feb 2025 1 repository listed
  Uniform Meaning Representation (UMR) is a recently developed graph-based semantic representation, which expands on Abstract Meaning Representation (AMR) in a number of ways, in particular through the inclusion of…
• BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning
  1 Oct 2022 1 repository listed
  Abstract Meaning Representation (AMR) offers a unified semantic representation for natural language sentences.
• Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
  8 Sep 2021 1 repository listed Syntology ran 4 of 11 samples · 7 unverified
  Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR.
• One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline
  18 May 2021 1 repository listed
  In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.
• Structural Adapters in Pretrained Language Models for AMR-to-text Generation
  16 Mar 2021 1 repository listed
  Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation.
• Online Back-Parsing for AMR-to-Text Generation
  9 Oct 2020 1 repository listed
  AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph.
• Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
  9 Oct 2020 1 repository listed
  With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.
• GPT-too: A language-model-first approach for AMR-to-text generation
  18 May 2020 1 repository listed Syntology ran 2 of 6 samples · 4 unverified
  Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
• Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
  8 Apr 2020 1 repository listed
  Our generated text has a significantly better semantic fidelity than the state of the art across all four datasets
• Graph Transformer for Graph-to-Sequence Learning
  18 Nov 2019 1 repository listed Syntology ran 0 of 10 samples · 10 unverified
  The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons.
• Enhancing AMR-to-Text Generation with Dual Graph Representations
  1 Sep 2019 1 repository listed
  Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges.
• Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
  31 Aug 2019 1 repository listed
  Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.
• A Graph-to-Sequence Model for AMR-to-Text Generation
  7 May 2018 1 repository listed
  The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.