242 citations · 1.5k across the 60 of their papers we have counts for
7 papers · 1 filter
Unified Pragmatic Models for Generating and Following Instructions
Daniel Fried, Jacob Andreas, Dan Klein
We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reas…
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu, Alex Irpan, Jacob Andreas +3
Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can…
Learning with Latent Language
Jacob Andreas, Dan Klein, Sergey Levine
The named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Ca…
Analogs of Linguistic Structure in Deep Representations
Jacob Andreas, Dan Klein
We investigate the compositional structure of message vectors computed by a deep network trained on a communication game. By comparing truth-conditional representations of encoder-…
A Minimal Span-Based Neural Constituency Parser
Mitchell Stern, Jacob Andreas, Dan Klein
In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with class…
Translating Neuralese
Jacob Andreas, Anca Dragan, Dan Klein
Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies…
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach +2
Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer…