Incorporating Copying Mechanism in Sequence-to-Sequence Learning

@article{Gu2016IncorporatingCM,
  title={Incorporating Copying Mechanism in Sequence-to-Sequence Learning},
  author={Jiatao Gu and Z. Lu and Hang Li and V. Li},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.06393}
}
We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. [...] Key Method 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. Our empirical study on both synthetic data sets and real world data sets…Expand
Sequential Copying Networks
Lexicon-constrained Copying Network for Chinese Abstractive Summarization
Copy that! Editing Sequences by Copying Spans
Improving Grapheme-to-Phoneme Conversion by Investigating Copying Mechanism in Recurrent Architectures
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Deep Reinforcement Learning for Sequence-to-Sequence Models
Efficient Summarization with Read-Again and Copy Mechanism
Marking Mechanism in Sequence-to-sequence Model for Mapping Language to Logical Form
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 22 REFERENCES
Sequence to Sequence Learning with Neural Networks
Pointer Networks
Neural Machine Translation by Jointly Learning to Align and Translate
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Pointing the Unknown Words
A Neural Conversational Model
Long Short-Term Memory
Neural Responding Machine for Short-Text Conversation
Grammar as a Foreign Language
...
1
2
3
...