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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]

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

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]

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]

A Neural Conversational Model   Oriol Vinyals, Quoc Le

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

A Diversity-Promoting Objective Function for Neural Conversation Models

A 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]

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\]

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

• [https://arxiv.org/abs/1512.05742]

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/]

INTERSPEECH 2017: INTERSPEECH 2017 which will take place on August 21-24 in Stockholm, Sweden, after SIGDIAL

YRRSDS 2017: Young Researchers’ Roundtable on Spoken Dialog Systems, which will take place on August 13-14 also in Saarbrücken, Germany, right before SIGDIAL.

SemDial 2017!

• [http://www.saardial.uni-saarland.de/]

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/]

Research Blog: Computer, respond to this email.

• [https://research.googleblog.com/2015/11/computer-respond-to-this-email.html]

Deep Learning for Chatbots, Part 1 – Introduction

• [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

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

Chatbot Fundamentals An interactive guide to writing bots in Python

• [https://apps.worldwritable.com/tutorials/chatbot/]

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]

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]

ChatterBot ChatterBot is a machine learning, conversational dialog engine for creating chat bots

• [http://chatterbot.readthedocs.io/]

DeepQA My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

• [https://github.com/Conchylicultor/DeepQA]

neuralconvo Neural conversational model in Torch

• [https://github.com/macournoyer/neuralconvo]

chatbot-rnn A toy chatbot powered by deep learning and trained on data from Reddit

• [https://github.com/pender/chatbot-rnn]

tf_seq2seq_chatbot tensorflow seq2seq chatbot

• [https://github.com/nicolas-ivanov/tf_seq2seq_chatbot]

ai-chatbot-framework A python chatbot framework with Natural Language Understanding and Artificial Intelligence.

• [https://github.com/alfredfrancis/ai-chatbot-framework]

DeepChatModels Conversation Models in Tensorflow

• [https://github.com/mckinziebrandon/DeepChatModels]

Chatbot Build your own chatbot base on IBM Watson

• [https://webchatbot.mybluemix.net/]

Chatbot An AI Based Chatbot

• [http://chatbot.sohelamin.com/]

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]

Chatbot 基於向量匹配的情境式聊天機器人

• [https://github.com/zake7749/Chatbot]

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]

Dialog_Corpus Datasets for Training Chatbot System

• [https://github.com/candlewill/Dialog_Corpus]

OpenSubtitles A series of scripts to download and parse the OpenSubtitles corpus.

• [https://github.com/AlJohri/OpenSubtitles]

insuranceqa-corpus-zh OpenData in insurance area for Machine Learning Tasks

• [https://github.com/Samurais/insuranceqa-corpus-zh]

dgk_lost_conv dgk_lost_conv 中文对白语料 chinese conversation corpus

[https://github.com/majoressense/dgk_lost_conv]

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]

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/]

Ming Zhou

• [https://www.microsoft.com/en-us/research/people/mingzhou/]

Jiwei Li(李纪为), - [http://web.stanford.edu/jiweil/]

Ryan Lowe, - [http://cs.mcgill.ca/rlowe1/]

Lili Mou

• [https://lili-mou.github.io/]

Jason Williams Microsoft

• [https://www.microsoft.com/en-us/research/people/jawillia/]

Bing Liu (刘冰) CMU

• [http://bingliu.me/]

Ian Lane

• [http://www.cs.cmu.edu/~ianlane/#&panel1-1]

Ondřej Dušek

• https://ufal.mff.cuni.cz/ondrej-dusek

Sungjin Lee 微软

• [https://www.microsoft.com/en-us/research/people/sule/]

Zhou Yu   俞舟 CMU

• [http://www.cs.cmu.edu/~zhouyu/]

华为诺亚实验室

• [http://www.noahlab.com.hk/topics/ShortTextConversation]

刘挺 哈尔滨工业大学

• [http://ir.hit.edu.cn/~tliu]

张伟男 哈尔滨工业大学  - [http://ir.hit.edu.cn/~wnzhang]

Wei Wu (武威) 微软

• [https://www.microsoft.com/en-us/research/people/wuwei/]

赵军 中科院自动化所

• [http://www.nlpr.ia.ac.cn/cip/jzhao.htm]

黄民烈 清华

• [http://aihuang.org/p/]

---

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