Search Results for author: Daniel Hewlett

Found 4 papers, 1 papers with code

Accurate Supervised and Semi-Supervised Machine Reading for Long Documents

no code implementations EMNLP 2017 Daniel Hewlett, Llion Jones, Alex Lacoste, re, Izzeddin Gur

We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows.

Question Answering Reading Comprehension

Coarse-to-Fine Question Answering for Long Documents

no code implementations ACL 2017 Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alex Lacoste, re, Jonathan Berant

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.

Question Answering Reading Comprehension +1

Hierarchical Question Answering for Long Documents

no code implementations6 Nov 2016 Eunsol Choi, Daniel Hewlett, Alexandre Lacoste, Illia Polosukhin, Jakob Uszkoreit, Jonathan Berant

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.

Question Answering Reading Comprehension +1

WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

2 code implementations ACL 2016 Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, David Berthelot

The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs).

Document Classification General Classification +2

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