Chatbot,聊天机器人。 chatbot是场交互革命,也是一个多技术融合的平台。上图给出了构建一个chatbot需要具备的组件,简单地说chatbot = NLU(Natural Language Understanding) + NLG(Natural Language Generation)。

知识荟萃

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)
  2. 微软邓力:对话系统的分类与发展历程
  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
  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
  5. 自己动手做聊天机器人教程(1-42)
  6. 如何让人工智能助理杜绝“智障” 微软亚洲研究院
  7. 周明:自然语言对话引擎 微软亚洲研究院
  8. 谢幸:用户画像、性格分析与聊天机器人
  9. 25 Chatbot Platforms: A Comparative Table
  10. 聊天机器人开发指南 IBM
  11. 朱小燕:对话系统中的NLP
  12. 使用深度学习打造智能聊天机器人 张俊林
  13. 九款工具帮您打造属于自己的聊天机器人
  14. 聊天机器人中对话模板的高效匹配方法
  15. 中国计算机学会通讯 2017年第9期 人机对话专刊
  • 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
  • 对话系统评价技术进展及展望 by 张伟男 车万翔
  • [https://pan.baidu.com/s/1o8Lv138]
  1. 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]

综述

  1. The Dialog State Tracking Challenge Series: A Review
  2. A Survey of Available Corpora for Building Data-Driven Dialogue Systems
  3. 任务型人机对话系统中的认知技术——— 概念、进展及其未来

进阶论文

  1. Sequence to Sequence Learning with Neural Networks
  2. A Neural Conversational Model Oriol Vinyals, Quoc Le
  3. A Diversity-Promoting Objective Function for Neural Conversation Models
  4. A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
  5. Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
  6.  A Persona-Based Neural Conversation Model
  7. Deep Reinforcement Learning for Dialogue Generation
  8.  End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning
  9. A Network-based End-to-End Trainable Task-oriented Dialogue System
  10.  Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems
  11. A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
  12. A Dataset for Research on Short-Text Conversation
  13. The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
  14. Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks, 2016
  15. Neural Utterance Ranking Model for Conversational Dialogue Systems, 2016
  16. A Context-aware Natural Language Generator for Dialogue Systems, 2016
  17. Task Lineages: Dialog State Tracking for Flexible Interaction, 2016
  18. Affective Neural Response Generation
  19. Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
  20. Chatbot Evaluation and Database Expansion via Crowdsourcing
  21. A Neural Network Approach for Knowledge-Driven Response Generation
  22. Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus
  23. Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory ACL 2017
  24. Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
  25. Augmenting End-to-End Dialog Systems with Commonsense Knowledge
  26. Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
  27. Attention with Intention for a Neural Network Conversation Model
  28. Response Selection with Topic Clues for Retrieval-based Chatbots
  29. LSTM based Conversation Models
  30. Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
  31. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders ACL 2017
  1. Words Or Characters? Fine-Grained Gating For Reading Comprehension ACL 2017

专门会议

  1. SIGDIAL ACL所属的关于对话系统的兴趣小组
  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!
  5. Dialog System Technology Challenge (DSTC)

Tutorial

  1. 2017 Tutorial - Deep Learning for Dialogue Systems ACL 2017
  2. Research Blog: Computer, respond to this email.
  3. Deep Learning for Chatbots, Part 1 – Introduction
  4. Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow
  5. Chatbot Fundamentals An interactive guide to writing bots in Python
  6. Chatbot Tutorial

软件

Chatbot

  1. ParlAI A framework for training and evaluating AI models on a variety of openly available dialog datasets.
  2. stanford-tensorflow-tutorials A neural chatbot using sequence to sequence model with attentional decoder.
  3. ChatterBot ChatterBot is a machine learning, conversational dialog engine for creating chat bots
  4. DeepQA My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot
  5. neuralconvo Neural conversational model in Torch
  6. chatbot-rnn A toy chatbot powered by deep learning and trained on data from Reddit
  7. tf_seq2seq_chatbot tensorflow seq2seq chatbot
  8. ai-chatbot-framework A python chatbot framework with Natural Language Understanding and Artificial Intelligence.
  9. DeepChatModels Conversation Models in Tensorflow
  10. Chatbot Build your own chatbot base on IBM Watson
  11. Chatbot An AI Based Chatbot
  12. neural-chatbot A chatbot based on seq2seq architecture done with tensorflow.

Chinese_Chatbot

  1. Seq2Seq_Chatbot_QA 使用TensorFlow实现的Sequence to Sequence的聊天机器人模型
  2. Chatbot 基於向量匹配的情境式聊天機器人
  3. chatbot-zh-torch7 中文Neural conversational model in Torch

数据集

  1. Cornell Movie-Dialogs Corpus
  2. Dialog_Corpus Datasets for Training Chatbot System
  3. OpenSubtitles A series of scripts to download and parse the OpenSubtitles corpus.
  4. insuranceqa-corpus-zh OpenData in insurance area for Machine Learning Tasks
  5. dgk_lost_conv dgk_lost_conv 中文对白语料 chinese conversation corpus
  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。
  7. Ubuntu Dialogue Corpus

领域专家

  1. Cambridge Dialogue Systems Group Steve Young
  2. Ming Zhou
  3. Jiwei Li(李纪为), - [http://web.stanford.edu/jiweil/]
  4. Ryan Lowe, - [http://cs.mcgill.ca/rlowe1/]
  5. Lili Mou
  6. Jason Williams Microsoft
  7. Bing Liu (刘冰) CMU
  8. Ian Lane
  9. Ondřej Dušek
  10. Sungjin Lee 微软
  11. Zhou Yu   俞舟 CMU
  12. 华为诺亚实验室
  13. 刘挺 哈尔滨工业大学
  14. 张伟男 哈尔滨工业大学  - [http://ir.hit.edu.cn/~wnzhang]
  15. Wei Wu (武威) 微软
  1. 赵军 中科院自动化所
  2. 黄民烈 清华

初步版本,水平有限,有错误或者不完善的地方,欢迎大家提建议和补充,会一直保持更新,本文为专知内容组原创内容,未经允许不得转载,如需转载请发送邮件至fangquanyi@gmail.com 或 联系微信专知小助手(Rancho_Fang)

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VIP内容

题目: 在微软小冰做好玩儿的研究

报告人: 宋睿华 微软小冰首席科学家 微软(亚洲)互联网工程院

摘要: 与众多厂商投入问答或任务型对话不同,微软小冰选择深耕细作闲聊领域。有人认为,闲聊没有显而易见的用处,而我却被这种好玩儿的对话深深吸引。在这次讲座中,我想跟大家介绍小冰在最近一年里从模仿到创造再到多模态理解的一些成果,希望给大家展示一些机器学习能做的好玩儿的应用。今天,小冰已不仅是一个聊天机器人,它所代表的情感计算框架涵盖了长程对话、人工智能创造和多模态等多方面的研究课题,支撑着未来塑造不同类型和性格的AI beings(硅基人类)。

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最新论文

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

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