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Computer Science > Computation and Language

arXiv:2302.05900 (cs)
[Submitted on 12 Feb 2023]

Title:Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters

Authors:Sebastien Montella, Alexis Nasr, Johannes Heinecke, Frederic Bechet, Lina M. Rojas-Barahona
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Abstract:Text generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs). Myriad approaches have linearized the input graph as a sequence of tokens to fit the PLM tokenization requirements. Nevertheless, this transformation jeopardizes the structural integrity of the graph and is therefore detrimental to its resulting representation. To overcome this issue, Ribeiro et al. have recently proposed StructAdapt, a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks (GNNs). In this paper, we investigate the influence of Relative Position Embeddings (RPE) on AMR-to-Text, and, in parallel, we examine the robustness of StructAdapt. Through ablation studies, graph attack and link prediction, we reveal that RPE might be partially encoding input graphs. We suggest further research regarding the role of RPE will provide valuable insights for Graph-to-Text generation.
Comments: Accepted to EACL 2023 (Main)
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2302.05900 [cs.CL]
  (or arXiv:2302.05900v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2302.05900

Submission history

From: Sebastien Montella [view email]
[v1] Sun, 12 Feb 2023 12:43:36 UTC (6,953 KB)
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