128 citations · 131 across the 4 of their papers we have counts for
7 papers · 1 filter
Learning Sparse Prototypes for Text Generation
Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig
Prototype-driven text generation uses non-parametric models that first choose from a library of sentence "prototypes" and then modify the prototype to generate the output text. Whi…
A Probabilistic Formulation of Unsupervised Text Style Transfer
Junxian He, Xinyi Wang, Graham Neubig +1
We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel d…
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…
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models h…