[NLG] Pretraining for Conditional Generation with Pseudo Self Attention
摘要： 这篇文章的主要idea就是如何用现在已经有的语言模型，稍微引导一下生成基于某些条件的话语。如给个情感，生成一个带情感的句子。作者在GPT2的结构上稍微修改了self-attention机制，命名为Pseudo-Self，在decoder的时候引入外部控制，并且这样做能够尽量小的影响GPT2之前的训练参数，达到的很好的效果。 模型： 作者主要对比了前面两种工作，关于这两种工作不做阐述
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相关文章
- 1. [NLG] Zero-Shot Dialog Generation with Cross-Domain Latent Actions
- 2. 5.Conditional Generation by RNN & Attention(李宏毅)
- 3. 论文学习-ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation
- 4. A Variational U-Net for Conditional Appearance and Shape Generation
- 5. Self-Attention GAN 中的 self-attention 机制
- 6. CVPR2020《Exploring Self-attention for Image Recognition》
- 7. Exploring Self-attention for Image Recognition稿
- 8. 【AAAI2019】Exploring Answer Stance Detection with Recurrent Conditional Attention
- 9. attention(show, attention and tell: neural image caption generation with visual attention)
- 10. 【ICJAI2018】Commonsense Knowledge Aware Conversation Generation with Graph Attention