202 citations · 261 across the 18 of their papers we have counts for
6 papers · 1 filter
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation
Yuxi Feng, Xiaoyuan Yi, Xiting Wang +2
Self-training (ST) has prospered again in language understanding by augmenting the fine-tuning of pre-trained language models when labeled data is insufficient. However, it remains…
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
Jinyi Hu, Xiaoyuan Yi, Wenhao Li +2
Variational Auto-Encoder (VAE) has been widely adopted in text generation. Among many variants, recurrent VAE learns token-wise latent variables with each conditioned on the preced…
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention
Wenhao Li, Xiaoyuan Yi, Jinyi Hu +2
Recently, powerful Transformer architectures have proven superior in generating high-quality sentences. Nevertheless, these models tend to produce dull high-frequency phrases, seve…
Self-explaining deep models with logic rule reasoning
Seungeon Lee, Xiting Wang, Sungwon Han +3
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By "human precis…
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Zonghan Yang, Xiaoyuan Yi, Peng Li +2
Warning: this paper contains model outputs exhibiting offensiveness and biases. Recently pre-trained language models (PLMs) have prospered in various natural language generation (N…
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
Jinyi Hu, Xiaoyuan Yi, Wenhao Li +2
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks. However, due to the sequential nature of the text, auto-regressive de…