James Bradbury

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

2015 · arXiv (Cornell University)

Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a neural network architecture which processes input sequences and questions, forms …

Pointer Sentinel Mixture Models

2016 · arXiv (Cornell University)

Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even …

Learned in Translation: Contextualized Word Vectors

2017 · arXiv (Cornell University)

Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with …

Non-Autoregressive Neural Machine Translation

2017 · arXiv (Cornell University)

Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower …

Quasi-Recurrent Neural Networks

2016 · arXiv (Cornell University)

Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences. We introduce …