281 citations · 654 across the 17 of their papers we have counts for
6 papers · 1 filter
A Bilingual Generative Transformer for Semantic Sentence Embedding
John Wieting, Graham Neubig, Taylor Berg-Kirkpatrick
Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences…
Simple and Effective Paraphrastic Similarity from Parallel Translations
John Wieting, Kevin Gimpel, Graham Neubig +1
We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora.…
Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel +1
While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve…
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…
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…