2.7k citations · 4.2k across the 18 of their papers we have counts for
4 papers · 1 filter
The Neural Noisy Channel
Lei Yu, Phil Blunsom, Chris Dyer +2
We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct mo…
Semantic Parsing with Semi-Supervised Sequential Autoencoders
Tomáš Kočiský, Gábor Melis, Edward Grefenstette +4
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent se…
Optimizing Performance of Recurrent Neural Networks on GPUs
Jeremy Appleyard, Tomas Kocisky, Phil Blunsom
As recurrent neural networks become larger and deeper, training times for single networks are rising into weeks or even months. As such there is a significant incentive to improve…
Latent Predictor Networks for Code Generation
Wang Ling, Edward Grefenstette, Karl Moritz Hermann +4
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates…