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PARALLEL-HIERARCHICAL MODEL FOR MACHINE COMPREHENSION ON SMALL DATA
PARALLEL-HIERARCHICAL MODEL FOR MACHINE COMPREHENSION ON SMALL DATA
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机译:小数据机器综合的并行层次模型
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摘要
Examples of the present disclosure provide systems and methods relating to a machine comprehension test with a learning-based approach, harnessing neural networks arranged in a parallel hierarchy. This parallel hierarchy enables the model to compare the passage, question, and answer from a variety of perspectives, as opposed to using a manually designed set of features. Perspectives may range from the word level to sentence fragments to sequences of sentences, and networks operate on word-embedding representations of text. A training methodology for small data is also provided.
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