4 citations · 5 across the 3 of their papers we have counts for
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cs.CL2022★ 1 cited
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
Yanyang Li, Jianqiao Zhao, Michael R. Lyu +1
Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to levera…
cs.CL2022★ 4 cited
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
Wanyu Du, Jianqiao Zhao, Liwei Wang +1
Generating high quality texts with high diversity is important for many NLG applications, but current methods mostly focus on building deterministic models to generate higher quali…
cs.CL2021
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Pei Ke, Haozhe Ji, Yu Ran +5
Existing pre-trained models for knowledge-graph-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 la…