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- W2952328824 abstract "Automatic question generation is an important problem in natural language processing. In this paper, we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to generate more suitable questions adaptively from the input data. Our experimental results show the proposed model can outperform the state-of-the-art question generation methods in terms of BLEU and ROUGE evaluation scores." @default.
- W2952328824 created "2019-06-27" @default.
- W2952328824 creator A5035760052 @default.
- W2952328824 date "2019-06-25" @default.
- W2952328824 modified "2023-10-05" @default.
- W2952328824 title "Learning to Generate Questions with Adaptive Copying Neural Networks" @default.
- W2952328824 cites W2137006453 @default.
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- W2952328824 doi "https://doi.org/10.1145/3299869.3300100" @default.
- W2952328824 hasPublicationYear "2019" @default.
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