Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding

• URL: http://arxiv.org/abs/2505.08504v1
• Date: Tue, 13 May 2025 12:36:02 GMT
• Title: Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding
• Authors: Jeongwoo Kang, Maximin Coavoux, Cédric Lopez, Didier Schwab,
• Abstract summary: We argue that Penman encoding has limitations for deep AMR graphs.<n>We propose a triple-based linearization method and compare its efficiency with Penman linearization.
• Score: 8.04933271357397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al., 2013, AMR) parsers. To train such models, AMR graphs have to be linearized into a one-line text format. While Penman encoding is typically used for this purpose, we argue that it has limitations: (1) for deep graphs, some closely related nodes are located far apart in the linearized text (2) Penman's tree-based encoding necessitates inverse roles to handle node re-entrancy, doubling the number of relation types to predict. To address these issues, we propose a triple-based linearization method and compare its efficiency with Penman linearization. Although triples are well suited to represent a graph, our results suggest room for improvement in triple encoding to better compete with Penman's concise and explicit representation of a nested graph structure.

Related papers

• Guiding AMR Parsing with Reverse Graph Linearization [45.37129580211495]
  We propose a novel Reverse Graph Linearization (RGL) framework for AMR parsing. RGL defines both default and reverse linearization orders of an AMR graph, where most structures at the back part of the default order appear at the front part of the reversed order and vice versa. Our analysis shows that our proposed method significantly mitigates the problem of structure loss accumulation, outperforming the previously best AMR parsing model by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 dataset, respectively.
  arXiv  Detail & Related papers  (2023-10-13T05:03:13Z)
• Node Copying: A Random Graph Model for Effective Graph Sampling [35.957719744856696]
  We introduce the node copying model for constructing a distribution over graphs. We show the usefulness of the copying model in three tasks. We employ our proposed model to mitigate the effect of adversarial attacks on the graph topology.
  arXiv  Detail & Related papers  (2022-08-04T04:04:49Z)
• Embedding Graphs on Grassmann Manifold [31.42901131602713]
  This paper develops a new graph representation learning scheme, namely EGG, which embeds approximated second-order graph characteristics into a Grassmann manifold. The effectiveness of EGG is demonstrated using both clustering and classification tasks at the node level and graph level.
  arXiv  Detail & Related papers  (2022-05-30T12:56:24Z)
• LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing [6.846638912020957]
  We show that better systematic generalization can be achieved by producing the meaning representation directly as a graph and not as a sequence. We propose LAGr, the Labeling Aligned Graphs algorithm that produces semantic parses by predicting node and edge labels for a complete multi-layer input-aligned graph.
  arXiv  Detail & Related papers  (2021-10-14T17:37:04Z)
• Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
  We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k) It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
  arXiv  Detail & Related papers  (2021-09-01T08:24:02Z)
• Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation [18.03898476141173]
  A graph generative model defines a distribution over graphs. We derive the exact joint probability over the graph and the node ordering of the sequential process. We train graph generative models by maximizing this bound, without using the ad-hoc node orderings of previous methods.
  arXiv  Detail & Related papers  (2021-06-11T06:37:52Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Promoting Graph Awareness in Linearized Graph-to-Text Generation [72.83863719868364]
  We study the ability of linearized models to encode local graph structures. Our findings motivate solutions to enrich the quality of models' implicit graph encodings. We find that these denoising scaffolds lead to substantial improvements in downstream generation in low-resource settings.
  arXiv  Detail & Related papers  (2020-12-31T18:17:57Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [53.31911669146451]
  Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks. These graphs are usually incomplete, urging auto-completion of them. graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings. textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations.
  arXiv  Detail & Related papers  (2020-04-30T13:50:34Z)
• Wasserstein-based Graph Alignment [56.84964475441094]
  We cast a new formulation for the one-to-many graph alignment problem, which aims at matching a node in the smaller graph with one or more nodes in the larger graph. We show that our method leads to significant improvements with respect to the state-of-the-art algorithms for each of these tasks.
  arXiv  Detail & Related papers  (2020-03-12T22:31:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.