24 citations · 159 across the 20 of their papers we have counts for
4 papers · 1 filter
A Survey of Reinforcement Learning Informed by Natural Language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar +5
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it…
Pragmatically Informative Text Generation
Sheng Shen, Daniel Fried, Jacob Andreas +1
We improve the informativeness of models for conditional text generation using techniques from computational pragmatics. These techniques formulate language production as a game be…
Good-Enough Compositional Data Augmentation
Jacob Andreas
We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic tr…
Measuring Compositionality in Representation Learning
Jacob Andreas
Many machine learning algorithms represent input data with vector embeddings or discrete codes. When inputs exhibit compositional structure (e.g. objects built from parts or proced…