2.7k citations · 5.7k across the 63 of their papers we have counts for
4 papers · 1 filter
Learning to Compose Words into Sentences with Reinforcement Learning
Dani Yogatama, Phil Blunsom, Chris Dyer +2
We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured m…
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…
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…