526 citations · 1.4k across the 19 of their papers we have counts for
6 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…
Reference-Aware Language Models
Zichao Yang, Phil Blunsom, Chris Dyer +1
We propose a general class of language models that treat reference as an explicit stochastic latent variable. This architecture allows models to create mentions of entities and the…
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…
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
Yulia Tsvetkov, Manaal Faruqui, Wang Ling +2
We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.…
Neural Network-Based Abstract Generation for Opinions and Arguments
Lu Wang, Wang Ling
We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple t…
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…