Search Results for author: Jeffrey Ling

Found 9 papers, 4 papers with code

Learning Cross-Context Entity Representations from Text

no code implementations11 Jan 2020 Jeffrey Ling, Nicholas FitzGerald, Zifei Shan, Livio Baldini Soares, Thibault Févry, David Weiss, Tom Kwiatkowski

Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.

Entity Linking Language Modelling +1

Fusion of Detected Objects in Text for Visual Question Answering

1 code implementation IJCNLP 2019 Chris Alberti, Jeffrey Ling, Michael Collins, David Reitter

To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language.

Question Answering Visual Commonsense Reasoning +1

Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories

no code implementations ICLR Workshop LLD 2019 Jeffrey Ling, Nicholas FitzGerald, Livio Baldini Soares, David Weiss, Tom Kwiatkowski

Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.

Entity Typing Language Modelling +1

Coarse-to-Fine Attention Models for Document Summarization

no code implementations WS 2017 Jeffrey Ling, Alex Rush, er

Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization.

Document Summarization Machine Translation +1

Image-to-Markup Generation with Coarse-to-Fine Attention

13 code implementations ICML 2017 Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush

We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism.

Optical Character Recognition

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