論文筆記：基於外部知識的會話模型Learning to Select Knowledge for Response Generation in Dialog Systems
Learning to Select Knowledge for Response Generation in Dialog Systems 1 出發點 傳統的Seq2Seq模型趨向產生一般的且信息含量較少的回答。 現有的具有外部知識的模型中，很少有人證明他們的模型有能力將適當的知識納入生成的回答中。 2 論文貢獻 在訓練階段，利用後驗知識來實現有效的知識選擇和整合，並且指導先驗知識分佈的訓練。在
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相關文章
- 1. 知識驅動對話-Learning to Select Knowledge for Response Generation in Dialog Systems-閱讀筆記
- 2. 論文筆記：基於外部知識的會話模型Commonsense Knowledge Aware Conversation Generation with Graph Attention
- 3. 論文筆記《Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning》
- 4. Wide & Deep Learning for Recommender Systems論文筆記
- 5. 論文筆記 - Wide & Deep Learning for Recommender Systems
- 6. 論文筆記：Wide & Deep Learning for Recommender Systems
- 7. 《Mem2Seq:Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems》閱讀筆記
- 8. 論文筆記：ACL 2019 Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
- 9. 論文筆記+模型實現TransNets: Learning to Transform for Recommendation
- 10. 論文閱讀筆記：Recent Advances and Challenges in Task-oriented Dialog Systems