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  • CONVOLUTION NEURAL NETS (CNN)
  • SEQ2SEQ SEQUENCE TO SEQUENCE

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  1. Natural Language Processing

Neural NLP

PreviousSEARCHNextTokenization

Last updated 2 years ago

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CONVOLUTION NEURAL NETS (CNN)

  1. - tal perry

SEQ2SEQ SEQUENCE TO SEQUENCE

  1. - char-level, token-using embedding layer, teacher forcing

  5. , and on

  6. , , and its

- In this paper, we incorporate copying into neural network-based Seq2Seq learning and propose a new model called CopyNet with encoder-decoder structure. CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence.

Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Cnn for text
1D CNN using KERAS
Keras blog
Teacher forcing explained
Same as keras but with token-level
Medium on char, word, byte-level
Mastery on enc-dec using the keras method
neural translation
Machine translation git from eng to jap
another
medium