聊天机器人Chatbot知识资料全集（入门/进阶/论文/软件/数据/专家等）(附pdf下载)

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• 聊天机器人 (Chatbot) 专知荟萃

• 入门学习

• 进阶论文

• 综述

• 专门会议

• Tutorial

• 软件

• Chatbot

• Chinese_Chatbot

• 数据集

• 领域专家

聊天机器人 (Chatbot) 专知荟萃

入门学习

1. 对话系统的历史(聊天机器人发展)

• [http://blog.csdn.net/zhoubl668/article/details/8490310]

2. 微软邓力：对话系统的分类与发展历程

• [https://www.leiphone.com/news/201703/6PNNwLXouKQ3EyI5.html]

3. Deep Learning for Chatbots, Part 1 – Introduction 聊天机器人中的深度学习技术之一：导读

• [http://www.jeyzhang.com/deep-learning-for-chatbots-1.html]

• [http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/]

4. Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow  聊天机器人中的深度学习技术之二：基于检索模型的实现

• [http://www.jeyzhang.com/deep-learning-for-chatbots-2.html]

• [http://www.wildml.com/2016/07/deep-learning-for-chatbots-2-retrieval-based-model-tensorflow/]

5. 自己动手做聊天机器人教程（1-42）

• [https://github.com/warmheartli/ChatBotCourse]

6. 如何让人工智能助理杜绝“智障”  微软亚洲研究院

• [http://www.msra.cn/zh-cn/news/features/virtual-personal-assistant-20170411]

7. 周明：自然语言对话引擎  微软亚洲研究院

• [http://www.msra.cn/zh-cn/news/features/ming-zhou-conversation-engine-20170413]

8. 谢幸：用户画像、性格分析与聊天机器人

• [http://www.msra.cn/zh-cn/news/features/xing-xie-speech-20170324]

9. 25 Chatbot Platforms: A Comparative Table

• [https://chatbotsjournal.com/25-chatbot-platforms-a-comparative-table-aeefc932eaff]

10. 聊天机器人开发指南   IBM

• [https://www.ibm.com/developerworks/cn/cognitive/library/cc-cognitive-chatbot-guide/index.html]

11. 朱小燕：对话系统中的NLP

• [http://mp.weixin.qq.com/s/JyQ34kBNh2M5avdDtL0k_Q]

12. 使用深度学习打造智能聊天机器人   张俊林

• [http://blog.csdn.net/malefactor/article/details/51901115]

13. 九款工具帮您打造属于自己的聊天机器人

• [http://mobile.51cto.com/hot-520148.htm]

14. 聊天机器人中对话模板的高效匹配方法

• [http://blog.csdn.net/malefactor/article/details/52166235]

15. 中国计算机学会通讯 2017年第9期   人机对话专刊

• 对话系统评价技术进展及展望                   by 张伟男 车万翔

• 人机对话                                                      by 刘 挺 张伟男

• 任务型与问答型对话系统中的语言理解技术  by 车万翔 张 宇

• 聊天机器人的技术及展望                            by 武 威 周 明

• 人机对话中的情绪感知与表达                     by 黄民烈 朱小燕

• 对话式交互与个性化推荐                            by 胡云华

• 对话智能与认知型口语交互界面                  by 俞 凯

• [https://pan.baidu.com/s/1o8Lv138]

16. 中国人工智能学会通讯

• 从图灵测试到智能信息获取                 郝 宇，朱小燕，黄民烈

• 智能问答技术                                       何世柱，张元哲，刘 康，赵 军

• 社区问答系统及相关技术                                 王 斌，吉宗诚

• 聊天机器人技术的研究进展                              张伟男，刘 挺

• 如何评价智能问答系统                                       黄萱菁

• 智能助手: 走出科幻，步入现实                        赵世奇，吴华

• [http://caai.cn/index.php?s=/Home/Article/qikandetail/year/2016/month/01.html]

  
  

进阶论文

1. Sequence to Sequence Learning with Neural Networks

• [http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf]

2. A Neural Conversational Model   Oriol Vinyals, Quoc Le

• [http://arxiv.org/pdf/1506.05869v1.pdf]

3. A Diversity-Promoting Objective Function for Neural Conversation Models

4. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

• [https://arxiv.org/abs/1605.06069]

5. Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

• [https://arxiv.org/abs/1607.00970]

6.  A Persona-Based Neural Conversation Model

• [https://arxiv.org/abs/1603.06155]

7. Deep Reinforcement Learning for Dialogue Generation

• [https://arxiv.org/abs/1606.01541]

8.  End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning

• [https://arxiv.org/abs/1606.01269]

9. A Network-based End-to-End Trainable Task-oriented Dialogue System

• [https://arxiv.org/abs/1604.04562]

10.  Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems

• [http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/871]

11. A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

• [https://arxiv.org/abs/1506.06714]

12. A Dataset for Research on Short-Text Conversation

• [http://staff.ustc.edu.cn/~cheneh/paper_pdf/2013/HaoWang.pdf\]

13. The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

• [https://arxiv.org/abs/1506.08909]

14. Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks, 2016

• [https://arxiv.org/abs/1609.01462]

15. Neural Utterance Ranking Model for Conversational Dialogue Systems, 2016

• [https://www.researchgate.net/publication/312250877_Neural_Utterance_Ranking_Model_for_Conversational_Dialogue_Systems\

16. A Context-aware Natural Language Generator for Dialogue Systems, 2016

• [https://arxiv.org/abs/1608.07076]

17. Task Lineages: Dialog State Tracking for Flexible Interaction, 2016

• [https://www.microsoft.com/en-us/research/publication/task-lineages-dialog-state-tracking-flexible-interaction-2/]

18. Affective Neural Response Generation

• [https://arxiv.org/abs/1709.03968]

19. Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

• [https://arxiv.org/abs/1710.07388]

20. Chatbot Evaluation and Database Expansion via Crowdsourcing

• [http://www.cs.cmu.edu/afs/cs/user/zhouyu/www/LREC.pdf]

21. A Neural Network Approach for Knowledge-Driven Response Generation

• [http://www.aclweb.org/anthology/C16-1318]

22. Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

• [http://www.cs.toronto.edu/~lcharlin/papers/ubuntu_dialogue_dd17.pdf\]

23. Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory  ACL 2017

• [https://arxiv.org/abs/1704.01074]

24. Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

• [https://arxiv.org/abs/1709.04264]

25. Augmenting End-to-End Dialog Systems with Commonsense Knowledge

• [https://arxiv.org/abs/1709.05453]

26. Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

• [https://arxiv.org/abs/1511.06931]

27. Attention with Intention for a Neural Network Conversation Model

• [https://arxiv.org/abs/1510.08565]

28. Response Selection with Topic Clues for Retrieval-based Chatbots

• [https://arxiv.org/abs/1605.00090]

29. LSTM based Conversation Models

• [https://arxiv.org/abs/1603.09457]

30. Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

• [https://arxiv.org/abs/1704.08966]

31. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders   ACL 2017

• [https://arxiv.org/abs/1703.10960]

32. Words Or Characters? Fine-Grained Gating For Reading Comprehension    ACL 2017

• [https://arxiv.org/abs/1611.01724v1]

转自：专知

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