41 citations · 84 across the 6 of their papers we have counts for
8 papers
SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels
Chenliang Li, Ming Yan, Haiyang Xu +4
Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed rapid progress for learning cross-modal representations. Existing pre-training methods eit…
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Fuli Luo, Pengcheng Yang, Shicheng Li +2
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typica…
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Damai Dai, Hua Zheng, Fuli Luo +3
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of…
Pun-GAN: Generative Adversarial Network for Pun Generation
Fuli Luo, Shunyao Li, Pengcheng Yang +4
In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide th…
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Chen Wu, Xuancheng Ren, Fuli Luo +1
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) th…
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Fuli Luo, Peng Li, Jie Zhou +4
Unsupervised text style transfer aims to transfer the underlying style of text but keep its main content unchanged without parallel data. Most existing methods typically follow two…