152 citations · 266 across the 21 of their papers we have counts for
6 papers · 1 filter
A Deep Factorization of Style and Structure in Fonts
Nikita Srivatsan, Jonathan T. Barron, Dan Klein +1
We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph int…
Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following
David Gaddy, Dan Klein
We consider the problem of learning to map from natural language instructions to state transitions (actions) in a data-efficient manner. Our method takes inspiration from the idea…
Cross-Domain Generalization of Neural Constituency Parsers
Daniel Fried, Nikita Kitaev, Dan Klein
Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains? We present three results a…
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
Ronghang Hu, Daniel Fried, Anna Rohrbach +3
Vision-and-Language Navigation (VLN) requires grounding instructions, such as "turn right and stop at the door", to routes in a visual environment. The actual grounding can connect…
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…
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Nikita Kitaev, Dan Klein
We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectu…