4.7k citations · 10.6k across the 33 of their papers we have counts for
8 papers · 1 filter
Simple and Effective Noisy Channel Modeling for Neural Machine Translation
Kyra Yee, Nathan Ng, Yann N. Dauphin +1
Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. This makes decoding decisions based on pa…
Pay Less Attention with Lightweight and Dynamic Convolutions
Felix Wu, Angela Fan, Alexei Baevski +2
Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current t…
Strategies for Structuring Story Generation
Angela Fan, Mike Lewis, Yann Dauphin
Writers generally rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for c…
Hierarchical Neural Story Generation
Angela Fan, Mike Lewis, Yann Dauphin
We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired wit…
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier +2
The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture base…
Language Modeling with Gated Convolutional Networks
Yann N. Dauphin, Angela Fan, Michael Auli +1
The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded conte…