Papers with semantics-oriented attention

    1 papers
    Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching (D19-1)

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    Challenge: Sentence matching is a key issue in natural language inference and paraphrase identification.
    Approach: They propose a semantics-oriented attention and deep fusion network (OSOA-DFN) that is oriented to the original semantic representation of another sentence and propagates attention information at each matching layer.
    Outcome: The proposed model can model sentence matching more precisely on three sentence matching benchmark datasets.

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