JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
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Existing pre-trained models for knowledgegraph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments.To tackle these problems, we propose a graph-text joint representation learning model called JointGT.During encoding, we devise a structure-aware semantic aggregation module which is plugged into each Transformer layer to preserve the graph structure.Furthermore, we propose three new pre-training tasks to explicitly enhance the graph-text alignment including respective text / graph reconstruction, and graph-text alignment in the embedding space via Optimal Transport.Experiments show that JointGT obtains new stateof-the-art performance on various KG-to-text datasets 1 .
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Tasks are more difficult; more abstract multimodal tasks are proposed.
“Tasks are more difficult.Beyond the straightforward tasks, more abstract multimodal tasks are proposed, e.g., MultiMET [153] (a multimodal dataset for metaphor understanding), Hateful Memes [154] (hate speech in multimodal memes).”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [154]
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Self-citationFew-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
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Question Answering over Knowledge Graphs via Machine Reading Comprehension
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Complex Reasoning in Natural Languag
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Generative Question Answering Systems over Knowledge Graphs and Tex
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12 Shades of RDF: Impact of Syntaxes on Data Extraction with Language Models
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Efficient Incorporation of Knowledge Graph Information for Enhanced Graph-to-Text Generation
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Graph and Question Interaction Aware Graph2Seq Model for Knowledge Base Question Generation
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Learning from Multiple Sources for Data-to-Text and Text-to-DataPreprint
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- Authors
- Ke, P., Ji, H., Ran, Y., Cui, X., Wang, L., Song, L., Zhu, X., & Huang, M.
- Affiliations
- State Key Laboratory of Intelligent Technology and Systems (CN)
- Chinese University of Hong Kong (HK)
- Tencent (China) (CN)
- Corresponding author
- Huang, Minlie
- Venue
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Published
- 2021
- Pages
- 2526-2538
- OpenAlex
- W3176175717
- Cited by
- 92
- References
- 56
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 17 · 2023: 31 · 2022: 11 · 2021: 1
- FWCI
- 6.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61876096, 61936010)
- Tsinghua University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- pre-training tasks, textual reconstruction, graph reconstruction, optimal transport
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 ›
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