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• W2766508367 abstract "Common-sense and background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, this knowledge must be acquired from training corpora during learning, and then it is static at test time. We introduce a new architecture for the dynamic integration of explicit background knowledge in NLU models. A general-purpose reading module reads background knowledge in the form of free-text statements (together with task-specific text inputs) and yields refined word representations to a task-specific NLU architecture that reprocesses the task inputs with these representations. Experiments on document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of the approach. Analysis shows that our model learns to exploit knowledge in a semantically appropriate way." @default.
• W2766508367 created "2017-11-10" @default.
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• W2766508367 date "2017-06-08" @default.
• W2766508367 modified "2023-10-16" @default.
• W2766508367 title "Dynamic Integration of Background Knowledge in Neural NLU Systems" @default.
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