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• W2772136736 abstract "Reading a document and extracting an answer to a question about its content has attracted substantial attention recently. While most work has focused on the interaction between the question and the document, in this work we evaluate the importance of context when the question and document are processed independently. We take a standard neural architecture for this task, and show that by providing rich contextualized word representations from a large pre-trained language model as well as allowing the model to choose between context-dependent and context-independent word representations, we can obtain dramatic improvements and reach performance comparable to state-of-the-art on the competitive SQuAD dataset." @default.
• W2772136736 created "2017-12-22" @default.
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• W2772136736 date "2017-12-10" @default.
• W2772136736 modified "2023-09-27" @default.
• W2772136736 title "Contextualized Word Representations for Reading Comprehension" @default.
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• W2772136736 hasPublicationYear "2017" @default.
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