152 citations · 305 across the 25 of their papers we have counts for
5 papers · 2 filters
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…
Effective Inference for Generative Neural Parsing
Mitchell Stern, Daniel Fried, Dan Klein
Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited t…
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-…
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Daniel Fried, Mitchell Stern, Dan Klein
Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficul…
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…