MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity

نویسندگان

  • Wenpeng Yin
  • Hinrich Schütze
چکیده

We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of representations: shorter sequences in one chunk can be directly compared to longer sequences in the other chunk. MultiGranCNN also contains a flexible and modularized match feature component that is easily adaptable to different types of chunk matching. We demonstrate stateof-the-art performance of MultiGranCNN on clause coherence and paraphrase identification tasks.

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تاریخ انتشار 2015