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Thesis

Deep learning for reading and understanding language

Abstract:

This thesis presents novel tasks and deep learning methods for machine reading comprehension and question answering with the goal of achieving natural language understanding.

First, we consider a semantic parsing task where the model understands sentences and translates them into a logical form or instructions. We present a novel semi-supervised sequential autoencoder that considers language as a discrete sequential latent variable and semantic parses as the observations. This mod...

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Division:
MPLS
Department:
Computer Science
Role:
Author

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Role:
Supervisor
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Name:
EPSRC
Funding agency for:
Kočiský, T
Grant:
OUCL/2012/TK
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
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UUID:
uuid:cc45e366-cdd8-495b-af42-dfd726700ff0
Deposit date:
2018-10-18

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