23 citations · 59 across the 6 of their papers we have counts for
5 papers · 1 filter
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier,…
Improving Text Generation with Student-Forcing Optimal Transport
Guoyin Wang, Chunyuan Li, Jianqiao Li +10
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, how…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen, Yizhe Zhang, Ruiyi Zhang +7
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word…
Adversarial Text Generation via Feature-Mover's Distance
Liqun Chen, Shuyang Dai, Chenyang Tao +5
Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text…