论文笔记：基于外部知识的会话模型Learning to Select Knowledge for Response Generation in Dialog Systems
Learning to Select Knowledge for Response Generation in Dialog Systems 1 出发点 传统的Seq2Seq模型趋向产生一般的且信息含量较少的回答。 现有的具有外部知识的模型中，很少有人证明他们的模型有能力将适当的知识纳入生成的回答中。 2 论文贡献 在训练阶段，利用后验知识来实现有效的知识选择和整合，并且指导先验知识分布的训练。在
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相关文章
- 1. 知识驱动对话-Learning to Select Knowledge for Response Generation in Dialog Systems-阅读笔记
- 2. 论文笔记：基于外部知识的会话模型Commonsense Knowledge Aware Conversation Generation with Graph Attention
- 3. 论文笔记《Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning》
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- 7. 《Mem2Seq:Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems》阅读笔记
- 8. 论文笔记：ACL 2019 Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
- 9. 论文笔记+模型实现TransNets: Learning to Transform for Recommendation
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