281 citations · 873 across the 21 of their papers we have counts for
6 papers · 1 filter
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text
Bohan Li, Junxian He, Graham Neubig +2
When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are tra…
Revisiting Self-Training for Neural Sequence Generation
Junxian He, Jiatao Gu, Jiajun Shen +1
Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's predi…
The Source-Target Domain Mismatch Problem in Machine Translation
Jiajun Shen, Peng-Jen Chen, Matt Le +5
While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only…
Choosing Transfer Languages for Cross-Lingual Learning
Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee +10
Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance…
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
Junxian He, Zhisong Zhang, Taylor Berg-Kirkpatrick +1
Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant la…
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig +1
The variational autoencoder (VAE) is a popular combination of deep latent variable model and accompanying variational learning technique. By using a neural inference network to app…