MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models

@inproceedings{Pan2018MacNetTK,
  title={MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models},
  author={Boyuan Pan and Yazheng Yang and Hao Li and Zhou Zhao and Yueting Zhuang and Deng Cai and Xiaofei He},
  booktitle={NeurIPS},
  year={2018}
}
Machine Comprehension (MC) is one of the core problems in natural language processing, requiring both understanding of natural language and knowledge about the world. Rapid progress has been made since the release of several benchmark datasets, and recently the state-of-the-art models even surpass human performance on the well-known SQuAD evaluation. In this paper, we transfer knowledge learned from machine comprehension to the sequence-to-sequence tasks to deepen the understanding of the text… CONTINUE READING

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