Relation/Entity-Centric Reading Comprehension
Constructing a machine that understands human language is one of the most elusive and long-standing challenges in artificial intelligence. This thesis addresses this challenge through studies of reading comprehension with a focus on understanding entities and their relationships. More specifically, we focus on question answering tasks designed to measure reading comprehension. We focus on entities and relations because they are typically used to represent the semantics of natural language.
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Datasets
SQuAD
TriviaQA
HotpotQA
bAbI
NarrativeQA
MCTest
CBT
WikiReading
Who-did-What
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