[NLG]A Working Memory Model for Task-oriented Dialog Response Generation

时间 2021-01-02

标签 memNN dialogueSystem 繁體版

原文原文链接

总述： memNN常用于在对话中引入KB的知识，然而之前的工作大多数把dialogue history和KB的memory混在一起，作者借鉴了心理学的研究。有个work memory（类似于电脑内存的感觉）可以和两个记忆做交互，一个知识记忆（semantic memory），一个历史记忆（episodic memory）.然后作者就设计了一个模型，将对话历史作为历史记忆，将KB结构化知识作为知识记

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1. Tips for Generation in Encoder-Decoder model
2. Guiding the Long-Short Term Memory Model for Image Caption Generation
3. 知识驱动对话-Learning to Select Knowledge for Response Generation in Dialog Systems-阅读笔记
4. [Coling2018]SGM：Sequence Generation Model for Multi-Label Classification
5. Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
6. linux 3个内存模型(flat memory model、discontiguous memory model、sparse memory model)
7. 带你走进 JVM Memory Model
8. 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
9. CUDA ---- Memory Model
10. Neural Response Generation via GAN with an Approximate Embedding Layer

>>更多相关文章<<