【专知荟萃05】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)

【导读】主题荟萃知识是专知的核心功能之一，为用户提供AI领域系统性的知识学习服务。主题荟萃为用户提供全网关于该主题的精华（Awesome）知识资料收录整理，使得AI从业者便捷学习和解决工作问题！在专知人工智能主题知识树基础上，主题荟萃由专业人工编辑和算法工具辅助协作完成，并保持动态更新！另外欢迎对此创作主题荟萃感兴趣的同学，请加入我们专知AI创作者计划，共创共赢！ 今天专知为大家呈送第五篇专知主题荟萃-聊天机器人ChatBot知识资料全集荟萃 （入门/进阶/论文/软件/数据/专家等），请大家查看！专知访问www.zhuanzhi.ai, 或关注微信公众号后台回复" 专知"进入专知，搜索主题“chatbot”查看。欢迎转发分享！此外，我们也提供该文pdf下载链接，请文章末尾查看！

了解专知，专知，一个新的认知方式！

• 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]

综述

1. The Dialog State Tracking Challenge Series: A Review
   • [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/williams2016dstc_overview-1.pdf\]
2. A Survey of Available Corpora for Building Data-Driven Dialogue Systems
   • [https://arxiv.org/abs/1512.05742]
3. 任务型人机对话系统中的认知技术——— 概念、进展及其未来
   • [http://cjc.ict.ac.cn/online/cre/yk-2015112465445-20151210162142.pdf]

专门会议

1. SIGDIAL ACL所属的关于对话系统的兴趣小组
   • [http://www.sigdial.org/workshops/conference18/]
2. INTERSPEECH 2017: INTERSPEECH 2017 which will take place on August 21-24 in Stockholm, Sweden, after SIGDIAL
3. YRRSDS 2017: Young Researchers’ Roundtable on Spoken Dialog Systems, which will take place on August 13-14 also in Saarbrücken, Germany, right before SIGDIAL.
4. SemDial 2017!
   • [http://www.saardial.uni-saarland.de/]
5. Dialog System Technology Challenge (DSTC)
   • [https://www.microsoft.com/en-us/research/event/dialog-state-tracking-challenge/]
   • [https://github.com/mesnilgr/is13]

Tutorial

1. 2017 Tutorial - Deep Learning for Dialogue Systems ACL 2017
   • [https://sites.google.com/site/deeplearningdialogue/]
2. Research Blog: Computer, respond to this email.
   • [https://research.googleblog.com/2015/11/computer-respond-to-this-email.html]
3. Deep Learning for Chatbots, Part 1 – Introduction
   • [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
   • [http://www.wildml.com/2016/07/deep-learning-for-chatbots-2-retrieval-based-model-tensorflow/]
5. Chatbot Fundamentals An interactive guide to writing bots in Python
   • [https://apps.worldwritable.com/tutorials/chatbot/]
6. Chatbot Tutorial
   • [https://www.codeproject.com/Articles/36106/Chatbot-Tutorial#intro]

软件

Chatbot

1. ParlAI A framework for training and evaluating AI models on a variety of openly available dialog datasets.
   • [https://github.com/facebookresearch/ParlAI]
2. stanford-tensorflow-tutorials A neural chatbot using sequence to sequence model with attentional decoder.
   • [https://github.com/chiphuyen/stanford-tensorflow-tutorials/tree/master/assignments/chatbot]
3. ChatterBot ChatterBot is a machine learning, conversational dialog engine for creating chat bots
   • [http://chatterbot.readthedocs.io/]
4. DeepQA My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot
   • [https://github.com/Conchylicultor/DeepQA]
5. neuralconvo Neural conversational model in Torch
   • [https://github.com/macournoyer/neuralconvo]
6. chatbot-rnn A toy chatbot powered by deep learning and trained on data from Reddit
   • [https://github.com/pender/chatbot-rnn]
7. tf_seq2seq_chatbot tensorflow seq2seq chatbot
   • [https://github.com/nicolas-ivanov/tf_seq2seq_chatbot]
8. ai-chatbot-framework A python chatbot framework with Natural Language Understanding and Artificial Intelligence.
   • [https://github.com/alfredfrancis/ai-chatbot-framework]
9. DeepChatModels Conversation Models in Tensorflow
   • [https://github.com/mckinziebrandon/DeepChatModels]
10. Chatbot Build your own chatbot base on IBM Watson
    • [https://webchatbot.mybluemix.net/]
11. Chatbot An AI Based Chatbot
    • [http://chatbot.sohelamin.com/]
12. neural-chatbot A chatbot based on seq2seq architecture done with tensorflow.
    • [https://github.com/inikdom/neural-chatbot]

Chinese_Chatbot

1. Seq2Seq_Chatbot_QA 使用TensorFlow实现的Sequence to Sequence的聊天机器人模型
   • [https://github.com/qhduan/Seq2Seq_Chatbot_QA]
2. Chatbot 基於向量匹配的情境式聊天機器人
   • [https://github.com/zake7749/Chatbot]
3. chatbot-zh-torch7 中文Neural conversational model in Torch
   • [https://github.com/majoressense/chatbot-zh-torch7]

数据集

1. Cornell Movie-Dialogs Corpus
   • [http://www.cs.cornell.edu/cristian/CornellMovie-DialogsCorpus.html]
2. Dialog_Corpus Datasets for Training Chatbot System
   • [https://github.com/candlewill/Dialog_Corpus]
3. OpenSubtitles A series of scripts to download and parse the OpenSubtitles corpus.
   • [https://github.com/AlJohri/OpenSubtitles]
4. insuranceqa-corpus-zh OpenData in insurance area for Machine Learning Tasks
   • [https://github.com/Samurais/insuranceqa-corpus-zh]
5. dgk_lost_conv dgk_lost_conv 中文对白语料 chinese conversation corpus
   • [https://github.com/majoressense/dgk_lost_conv]
6. Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems 一共 1369 段对话，平均每段对话 15 轮。
   • [http://datasets.maluuba.com/Frames]
7. Ubuntu Dialogue Corpus
   • [http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/]

领域专家

1. Cambridge Dialogue Systems Group Steve Young
   • [http://mi.eng.cam.ac.uk/research/dialogue/]
2. Ming Zhou
   • [https://www.microsoft.com/en-us/research/people/mingzhou/]
3. Jiwei Li(李纪为), - [http://web.stanford.edu/jiweil/]
4. Ryan Lowe, - [http://cs.mcgill.ca/rlowe1/]
5. Lili Mou
   • [https://lili-mou.github.io/]
6. Jason Williams Microsoft
   • [https://www.microsoft.com/en-us/research/people/jawillia/]
7. Bing Liu (刘冰) CMU
   • [http://bingliu.me/]
8. Ian Lane
   • [http://www.cs.cmu.edu/~ianlane/#&panel1-1]
9. Ondřej Dušek
   • https://ufal.mff.cuni.cz/ondrej-dusek
10. Sungjin Lee 微软
    • [https://www.microsoft.com/en-us/research/people/sule/]
11. Zhou Yu 俞舟 CMU
    • [http://www.cs.cmu.edu/~zhouyu/]
12. 华为诺亚实验室
    • [http://www.noahlab.com.hk/topics/ShortTextConversation]
13. 刘挺 哈尔滨工业大学
    • [http://ir.hit.edu.cn/~tliu]
14. 张伟男 哈尔滨工业大学 - [http://ir.hit.edu.cn/~wnzhang]
15. Wei Wu (武威) 微软
    • [https://www.microsoft.com/en-us/research/people/wuwei/]
16. 赵军 中科院自动化所
    • [http://www.nlpr.ia.ac.cn/cip/jzhao.htm]
17. 黄民烈 清华
    • [http://aihuang.org/p/]

汇总不全面，欢迎补全和提建议，敬请关注http://www.zhuanzhi.ai 和关注专知公众号，获取最新AI相关知识