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

Daniel Hewlett
Alexandre Lacoste
Llion Jones
Illia Polosukhin
Andrew Fandrianto
Matthew Kelcey
David Berthelot
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany (2016)


We present WikiReading, a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the structured knowledge base Wikidata by reading the text of the corresponding Wikipedia articles. 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). We compare various state-of-the-art DNN-based architectures for document classification, information extraction, and question answering. We find that models supporting a rich answer space, such as word or character sequences, perform best. Our best-performing model, a word-level sequence to sequence model with a mechanism to copy out-of-vocabulary words, obtains an accuracy of 71.8%.

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