Papers about joint learning Graph and Text.
(1) [Arxiv] Efficient and effective training of language and graph neural network models [paper]
(2) [Arxiv] Natural Language is All a Graph Needs [paper]
(3) [Arxiv] Text Generation from Knowledge Graphs with Graph Transformers[paper][code]
(4) [Arxiv] JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs[paper][code]
(1) [Arxiv] Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning [paper]
(2) [ICLR23] LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS [paper]
(3) [Medium] Fine-tuning a GPT — LoRA [blog]
(4) [Youtube] Prefix-Tuning [Video]
(5) [Arxiv] Teach LLMs to Personalize – An Approach inspired by Writing Education [paper]
(6) [Arxiv] LaMP: When Large Language Models Meet Personalization [paper]
[ACL 2019] Graph Enhanced Cross-Domain Text-to-SQL Generation [paper]
[ACL 2020] Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks [paper]
[ACM] A Survey of Knowledge-enhanced Text Generation [paper]
[NAACL 2022] Text Generation from Knowledge Graphs with Graph Transformers [paper]
[EMNLP2021] Investigating Pretrained Language Models for Graph-to-Text Generation [paper]
[ACL 2021] JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs [paper]
[ACL 2019] Enhancing AMR-to-Text Generation with Dual Graph Representations [paper]
[NAACL 2019] Structural Neural Encoders for AMR-to-text Generation [paper]
[Survey] Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey [paper]
[ACL 2022] NGEP: A Graph-based Event Planning Framework for Story Generation [paper][code]
[] Content Planning for Neural Story Generation with Aristotelian Rescoring [paper][code]
[ICLR 2022] Graph Neural Networks with Learnable Structural and Positional Representations [paper] [Arxiv 2023] Graph Positional and Structural Encoder [paper]
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