2 citations · 4 across the 5 of their papers we have counts for
5 papers
Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Jian Guan, Zhenyu Yang, Rongsheng Zhang +2
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories a…
Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation
Pei Ke, Haozhe Ji, Zhenyu Yang +4
Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled…
LaMemo: Language Modeling with Look-Ahead Memory
Haozhe Ji, Rongsheng Zhang, Zhenyu Yang +2
Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in langu…
Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
Yinhe Zheng, Yida Wang, Pei Ke +2
This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiate…
The distance between the weights of the neural network is meaningful
Liqun Yang, Yijun Yang, Yao Wang +2
In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. To analyze the network's capacity, quantifying the…