Mixture Content Selection for Diverse Sequence Generation

@article{Cho2019MixtureCS,
  title={Mixture Content Selection for Diverse Sequence Generation},
  author={Jaemin Cho and Minjoon Seo and Hannaneh Hajishirzi},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.01953}
}
  • Jaemin Cho, Minjoon Seo, Hannaneh Hajishirzi
  • Published 2019
  • Computer Science
  • ArXiv
  • Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences. [...] Key Method The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection. The generation stage uses a standard encoder-decoder model given each selected content from the source sequence.Expand Abstract
    15 Citations

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    References

    SHOWING 1-10 OF 62 REFERENCES
    Get To The Point: Summarization with Pointer-Generator Networks
    • 1,430
    • Highly Influential
    • PDF
    Mixture Models for Diverse Machine Translation: Tricks of the Trade
    • 40
    • Highly Influential
    • PDF
    Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
    • 51
    • PDF
    Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
    • 4
    • PDF
    Bottom-Up Abstractive Summarization
    • 249
    • Highly Influential
    • PDF
    Diverse Beam Search for Improved Description of Complex Scenes
    • 58
    • Highly Influential
    • PDF
    Sequence to Sequence Mixture Model for Diverse Machine Translation
    • 25
    • PDF
    Analyzing Uncertainty in Neural Machine Translation
    • 83
    • Highly Influential
    • PDF
    Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
    • 349
    • PDF
    Multiple Choice Learning: Learning to Produce Multiple Structured Outputs
    • 105
    • PDF