GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation

• URL: http://arxiv.org/abs/2204.06674v4
• Date: Thu, 18 May 2023 14:36:32 GMT
• Title: GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation
• Authors: Anthony Colas, Mehrdad Alvandipour, Daisy Zhe Wang
• Abstract summary: Recent improvements in KG-to-text generation are due to auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. Here, we demonstrate that by fusing graph-aware elements into existing pre-trained language models, we are able to outperform state-of-the-art models and close the gap imposed by additional pre-training tasks.
• Score: 3.593955557310285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. These tasks require extensive computational resources while only suggesting marginal improvements. Here, we demonstrate that by fusing graph-aware elements into existing pre-trained language models, we are able to outperform state-of-the-art models and close the gap imposed by additional pre-training tasks. We do so by proposing a mask structure to capture neighborhood information and a novel type encoder that adds a bias to the graph-attention weights depending on the connection type. Experiments on two KG-to-text benchmark datasets show our models are competitive while involving fewer parameters and no additional pre-training tasks. By formulating the problem as a framework, we can interchange the various proposed components and begin interpreting KG-to-text generative models based on the topological and type information found in a graph.

Related papers

• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism [10.573861741540853]
  We propose a graph-guided self-attention mechanism, GraSAME, for pretrained language models. GraSAME seamlessly incorporates token-level structural information into PLMs without necessitating additional alignment or concatenation efforts. Our experiments on the graph-to-text generation task demonstrate that GraSAME outperforms baseline models and achieves results comparable to state-of-the-art (SOTA) models on WebNLG datasets.
  arXiv  Detail & Related papers  (2024-04-10T11:03:57Z)
• MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining [41.19687587548107]
  Graph Neural Networks (GNNs) need to be re-trained every time when applied to different graph tasks and datasets. We propose a novel framework MuseGraph, which seamlessly integrates the strengths of GNNs and Large Language Models (LLMs) Our experimental results demonstrate significant improvements in different graph tasks.
  arXiv  Detail & Related papers  (2024-03-02T09:27:32Z)
• Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining [62.069374456021016]
  We propose the Hierarchical Argumentation Graph (Hi-ArG), a new structure to organize arguments. We also introduce two approaches to exploit Hi-ArG, including a text-graph multi-modal model GreaseArG and a new pre-training framework augmented with graph information.
  arXiv  Detail & Related papers  (2023-12-01T19:03:38Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs [4.56877715768796]
  We show that ChatGPT achieves near state-of-the-art performance on some measures of the WebNLG 2020 challenge. We also show that there is a significant connection between what the LLM already knows about the data it is parsing and the quality of the output text.
  arXiv  Detail & Related papers  (2023-07-14T12:45:03Z)
• Stage-wise Fine-tuning for Graph-to-Text Generation [25.379346921398326]
  Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. We propose a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes model on Wikipedia before adapting to the graph-to-text generation.
  arXiv  Detail & Related papers  (2021-05-17T17:15:29Z)
• Structural Information Preserving for Graph-to-Text Generation [59.00642847499138]
  The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.
  arXiv  Detail & Related papers  (2021-02-12T20:09:01Z)
• 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)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

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.