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)

更新于 2017年11月6日 机器学习
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「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ChatbotChinese_Chatbot数据集领域专家对话系统的历史(聊天机器人发展)[http://blog.csdn.net/zhoubl668/article/details/8490310]微软邓力:对话系统的分类与发展历程[https://www.leiphone.com/news/201703/6PNNwLXouKQ3EyI5.html]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/]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/]自己动手做聊天机器人教程(1-42)[https://github.com/warmheartli/ChatBotCourse]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]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]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]25 Chatbot Platforms: A Comparative Table[https://chatbotsjournal.com/25-chatbot-platforms-a-comparative-table-aeefc932eaff]聊天机器人开发指南   IBM[https://www.ibm.com/developerworks/cn/cognitive/library/cc-cognitive-chatbot-guide/index.html]朱小燕:对话系统中的NLP[http://mp.weixin.qq.com/s/JyQ34kBNh2M5avdDtL0k_Q]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]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]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]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]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]Sequence to Sequence Learning with Neural Networks[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf]A Neural Conversational Model   Oriol Vinyals, Quoc Le[http://arxiv.org/pdf/1506.05869v1.pdf]A Diversity-Promoting Objective Function for Neural Conversation ModelsA Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues[https://arxiv.org/abs/1605.06069]Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation[https://arxiv.org/abs/1607.00970] A Persona-Based Neural Conversation Model[https://arxiv.org/abs/1603.06155]Deep Reinforcement Learning for Dialogue Generation[https://arxiv.org/abs/1606.01541] End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning[https://arxiv.org/abs/1606.01269]A Network-based End-to-End Trainable Task-oriented Dialogue System[https://arxiv.org/abs/1604.04562] Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems[http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/871]A Neural Network Approach to Context-Sensitive Generation of Conversational Responses[https://arxiv.org/abs/1506.06714]A Dataset for Research on Short-Text Conversation[http://staff.ustc.edu.cn/~cheneh/paper_pdf/2013/HaoWang.pdf\]The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems[https://arxiv.org/abs/1506.08909]Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks, 2016[https://arxiv.org/abs/1609.01462]Neural Utterance Ranking Model for Conversational Dialogue Systems, 2016[https://www.researchgate.net/publication/312250877_Neural_Utterance_Ranking_Model_for_Conversational_Dialogue_Systems\A Context-aware Natural Language Generator for Dialogue Systems, 2016[https://arxiv.org/abs/1608.07076]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/]Affective Neural Response Generation[https://arxiv.org/abs/1709.03968]Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models[https://arxiv.org/abs/1710.07388]Chatbot Evaluation and Database Expansion via Crowdsourcing[http://www.cs.cmu.edu/afs/cs/user/zhouyu/www/LREC.pdf]A Neural Network Approach for Knowledge-Driven Response Generation[http://www.aclweb.org/anthology/C16-1318]Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus[http://www.cs.toronto.edu/~lcharlin/papers/ubuntu_dialogue_dd17.pdf\]Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory  ACL 2017[https://arxiv.org/abs/1704.01074]Flexible End-to-End Dialogue System for Knowledge Grounded Conversation[https://arxiv.org/abs/1709.04264]Augmenting End-to-End Dialog Systems with Commonsense Knowledge[https://arxiv.org/abs/1709.05453]Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems[https://arxiv.org/abs/1511.06931]Attention with Intention for a Neural Network Conversation Model[https://arxiv.org/abs/1510.08565]Response Selection with Topic Clues for Retrieval-based Chatbots[https://arxiv.org/abs/1605.00090]LSTM based Conversation Models[https://arxiv.org/abs/1603.09457]Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models[https://arxiv.org/abs/1704.08966]Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders   ACL 2017[https://arxiv.org/abs/1703.10960]Words Or Characters? Fine-Grained Gating For Reading Comprehension    ACL 2017[https://arxiv.org/abs/1611.01724v1] 转自:专知 完整内容请点击“阅读原文” via: http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649678317&idx=3&sn=fe99119957aa4ee1737031fd36df7b0c&scene=0#wechat_redirect

 

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