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自动问答 Question Answering 专知荟萃

入门学习
进阶文章
Tutorial
综述
视频教程
代码
datasets
领域专家

自动问答 Question Answering 专知荟萃

入门学习

自动问答系统的类别冯志伟

[http://blog.sina.com.cn/s/blog_72d083c70102du8m.html\]

基于深度学习的智能问答周小强陈清财曾华军

[https://yq.aliyun.com/articles/58745]

基于知识图谱的电影自动问答系统（一）知识的获取与存储 (二）自动问答实现

[http://www.voidcn.com/article/p-hwhmeuje-ym.html]
[http://www.voidcn.com/article/p-wylzjird-ym.html]

客服系统机器人产品设计详解——智能回答

[http://www.chanpin100.com/article/105410]

聊天机器人与自动问答技术

[http://blog.csdn.net/heiyeshuwu/article/details/42965693]

揭开知识库问答KB-QA的面纱

[https://zhuanlan.zhihu.com/p/25735572]

Question answering with TensorFlow

[https://www.oreilly.com/ideas/question-answering-with-tensorflow]

进阶文章

Towards AI-Complete Question Answering: A set of prerequisite toy tasks

[http://arxiv.org/pdf/1502.05698v10.pdf]

Large Scale simple question answering with Memory Networks
- [https://arxiv.org/pdf/1506.02075v1.pdf]
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

[http://arxiv.org/pdf/1506.07285v5.pdf]

Key-Value Memory Networks for directly understanding documents
- [https://arxiv.org/pdf/1606.03126v1.pdf]
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

[https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ACL15-STAGG.pdf]

Value of Semantic Parse Labeling for KBQA

[https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/acl2016-webqsp.pdf]

Question Answering with Subgraph Embeddings

[https://arxiv.org/pdf/1406.3676v3.pdf]

Open Question Answering with Weakly Supervised Embedding Models
- [https://arxiv.org/pdf/1404.4326.pdf]
Learning End-to-End Goal-Oriented dialog

[https://arxiv.org/pdf/1605.07683v2.pdf]

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding

[https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/IS16_ContextualSLU.pdf]

Question Answering over Knowledge Base With Neural Attention Combining Global Knowledge Information

[https://arxiv.org/pdf/1606.00979v1.pdf]

Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Texts

[https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/acl2016relationpaths-1.pdf]

Neural Machine Translation by jointly learning to align and translate
- [https://arxiv.org/pdf/1409.0473v7.pdf]
Recurrent Neural Network Grammar

[https://arxiv.org/pdf/1602.07776v4.pdf]

Neural Turing Machines

[https://www.youtube.com/watch?v=_H0i0IhEO2g_)]

Teaching machines to read and comprehend

[https://arxiv.org/pdf/1506.03340.pdf]

Applying Deep Learning to answer selection: A study and an open task - [https://arxiv.org/pdf/1508.01585v2.pdf]
Reasoning with Neural Tensor Networks

[https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf]

Hybrid computing using a neural network with dynamic external memory

[http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)]

Gaussian Attention Model and its Application to Knowledge Base Embedding and Question Answering

[https://arxiv.org/pdf/1611.02266.pdf]

Gated Graph Sequence Neural Networks

[https://arxiv.org/abs/1511.05493]

Sequence to Sequence Learning With Neural Networks

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

Neural Conversation Model

[https://arxiv.org/pdf/1506.05869v1.pdf]

Query Reduction Networks For Question Answering

[https://arxiv.org/pdf/1606.04582.pdf]

Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

[https://arxiv.org/pdf/1606.01994.pdf]

Efficiently Answering Technical Questions — A Knowledge Graph Approach

[http://wangzhongyuan.com/en/papers/Technical_Questions_Answering.pdf]

An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge

[http://www.nlpr.ia.ac.cn/cip/~liukang/liukangPageFile/ACL2017-Hao.pdf]

Question Generation via Overgenerating Transformations and Ranking (Technical report)

[https://www.lti.cs.cmu.edu/sites/default/files/cmulti09013.pdf]

Automation of question generation from sentences

[http://www.sadidhasan.com/sadid-QG.pdf]

Good question!statistical ranking for question generation

[https://homes.cs.washington.edu/~nasmith/papers/heilman+smith.naacl10.pdf]

Question generation from paragraphs at upenn: Qgstec system description

[http://www.aclweb.org/anthology/I11-1104]

Automatically generating questions from queries for community-based question answering

[http://www.aclweb.org/anthology/I11-1104]

How to Generate Cloze Questions from Definitions: A Syntactic Approach - [https://www.cs.cmu.edu/~listen/pdfs/gates-2011-aaai-qg.pdf]
Generating natural language questions to support learning on-line
- [http://www.aclweb.org/anthology/W13-2114]
Deep questions without deep understanding
- [http://www.aclweb.org/anthology/P15-1086]
Semi-supervised qa with generative domain-adaptive nets

[https://pdfs.semanticscholar.org/e8a0/536dc080acd2ca83502dddd0d511ef3fbd8c.pdf]

Leveraging multiple views of text for automatic question generation

[http://link.springer.com/chapter/10.1007/978-3-319-19773-9_26]

Revup: Automatic gap-fill question generation from educational texts

[http://www.aclweb.org/anthology/W15-0618]

Towards topic-to-question generatio

[http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00206]

Ranking automatically generated questions using common human queries

[http://www.aclweb.org/old_anthology/W/W16/W16-66.pdf#page=233]

Generating quiz questions from knowledge graphs

[https://dl.acm.org/citation.cfm?doid=2740908.2742722]

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

[http://arxiv.org/pdf/1603.06807v1.pdf]

Knowledge Questions from Knowledge Graphs

[https://arxiv.org/abs/1610.09935]

Machine Comprehension by Text-to-Text Neural Question Generation
- [http://aclweb.org/anthology/W17-2603]
Question Generation from a Knowledge Base with Web Exploration
- [https://arxiv.org/pdf/1610.03807.pdf]
On Generating Characteristic-rich Question Sets for QA Evaluation
- [http://www.aclweb.org/anthology/D/D16/D16-1054.pdf]
Neural Question Generation from Text: A Preliminary Study

[https://arxiv.org/pdf/1704.01792.pdf]

Tutorial

Question Answering Become Main Theme of IR Research? 李航今日头条

[http://www.hangli-hl.com/uploads/3/4/4/6/34465961/airs_2016_question_answering.pdf\]
[http://lab.toutiao.com/index.php/2017/03/02/huaweilihangwill-question-answering-become-main-theme-of-research-in-information-retrieval.html]

深度问答技术中科院自动化所赵军老师

[链接：http://pan.baidu.com/s/1qYJV1Ti 密码：99c4]

深度学习与智能问答 CCL 2016 Tutorial 刘康冯岩松

基于传统符号表示的知识库问答
基于深度学习的知识库问答
基于深度学习的对话系统
基于深度学习的阅读解
[https://pan.baidu.com/s/1bpDAf8r]
[http://www.cips-cl.org/static/CCL2016/tutorialsT2B.html]

基于知识的智能问答技术冯岩松 2017. [http://cips-upload.bj.bcebos.com/2017/ssatt2017/ATT2017-QAI.pdf]
自动问答、聊天机器人与自然语言理解中国计算机学会《学科前沿讲习班》 by 严睿段楠段楠熊德意高剑峰谢幸 http://tcci.ccf.org.cn/conference/2017/adlnotice.php

综述

《Speeh and Language Processing》Chapter 28 Question Answering

[https://web.stanford.edu/~jurafsky/slp3/28.pdf\]

A Survey of Text Question Answering Techniques. Poonam Gupta,Vishal Gupta

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.258.7801&rep=rep1&type=pdf]

Question Answering Systems: Survey and Trends

[http://www.sciencedirect.com/science/article/pii/S1877050915034663]

The Question Answering Systems: A Survey

[https://www.researchgate.net/publication/311425566_The_Question_Answering_Systems_A_Survey\]

面向知识自动化的自动问答研究进展
[http://www.aas.net.cn/CN/10.16383/j.aas.2017.c160667]
自动问答综述 2002年 by 郑实福,刘挺,秦兵,李生 [http://jcip.cipsc.org.cn/CN/abstract/abstract1282.shtml]
基于 Web 的问答系统综述 2017 李舟军李水华 [http://www.jsjkx.com/jsjkx/ch/reader/create_pdf.aspx?file_no=20170601&flag=&journal_id=jsjkx&year_id=2017\]

视频教程

深度学习在自动问答系统中的应用李成华

[http://www.infoq.com/cn/presentations/deep-learing-on-automatic-question-answering-system]

代码

MemNN Impl Matlab

[https://github.com/facebook/MemNN]

Key Value MemNN

[https://github.com/siyuanzhao/key-value-memory-networks]

Quepy

[https://github.com/machinalis/quepy]

NLQuery

[https://github.com/ayoungprogrammer/nlquery]

ParlAI

[https://github.com/facebookresearch/ParlAI]

flask-chatterbot

[https://github.com/chamkank/flask-chatterbot]

Learning to Rank short text pairs with CNN SIGIR 2015

[https://github.com/shashankg7/Keras-CNN-QA]

TextKBQA

[https://github.com/rajarshd/TextKBQA]

BiAttnFlow

[https://github.com/allenai/bi-att-flow]

Datasets

SQuAD The Stanford Question Answering Dataset

[https://rajpurkar.github.io/SQuAD-explorer/]

CNN QA Task (Teaching Machines to Read & Comprehend)

[https://github.com/deepmind/rc-data/]

WebQuestions

[http://nlp.stanford.edu/software/sempre/]

Simple Questions

[https://research.facebook.com/research/babi]

Movie QA

[https://research.facebook.com/research/babi/]

WebQuestionsSP

[https://www.microsoft.com/en-us/download/details.aspx?id=52763]

WikiQA

[https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/YangYihMeek_EMNLP-15_WikiQA.pdf]

Kaggle AllenAI Challenge

[https://www.kaggle.com/c/the-allen-ai-science-challenge]

MC Test, Machine Comprehension Test Microsoft 2013

[http://research.microsoft.com/en-us/um/redmond/projects/mctest/]

MSR Sentence Completion Challenge

[https://www.microsoft.com/en-us/research/project/msr-sentence-completion-challenge/]

Dialog State Tracking Challenge

[http://camdial.org/~mh521/dstc/]

QA dataset featured in Teaching Machines to Read and Comprehend

[https://github.com/deepmind/rc-data/]

WebNav

[https://github.com/nyu-dl/WebNav/blob/master/README.md]

Stanford Question Answering Dataset

[https://rajpurkar.github.io/SQuAD-explorer/]

FB15K Knowledge Base

[https://www.microsoft.com/en-us/download/details.aspx?id=52312]

WikiQA

[http://aka.ms/WikiQA)]

Quora Duplicate Questions Dataset

[https://data.quora.com/)]

Query Reformulator Dataset Jeopardy etc

[https://github.com/nyu-dl/QueryReformulator)]

Quiz Bowl Questions

[https://www.cs.colorado.edu/~jbg/projects/IIS-1320538.html#Datasets]

WebQA-Chinese

[http://idl.baidu.com/WebQA.html]

Chat corpus

[https://github.com/Marsan-Ma/chat_corpus]

DeepMind Q&A Dataset - [http://cs.nyu.edu/~kcho/DMQA/]
WebQuestions - [https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a/#]
WebQA

http://idl.baidu.com/WebQA.html

GraphQuestions

https://github.com/ysu1989/GraphQuestions

领域专家

刘康博士，中科院自动化所模式识别国家重点实验室副研究员，中国中文信息学会青年工作委员会执行委员。研究领域包括信息抽取、网络挖掘、问答系统等，同时也涉及模式识别与机器学习方面的基础研究。在自然语言处理、知识工程等领域国际重要会议和期刊发表论文三十余篇（如TKDE、ACL、IJCAI、EMNLP、COLING、CIKM等），曾获KDD CUP 2011 Track2 全球亚军，COLING 2014最佳论文奖，首届“CCF-腾讯犀牛鸟基金卓越奖”、2014年度中国中文信息学会“钱伟长中文信息处理科学技术奖-汉王青年创新一等奖”、2015 Google Focused Research Award等。 - 个人主页：http://www.nlpr.ia.ac.cn/cip/~liukang/index.html
冯岩松博士，北京大学计算机科学与技术研究所讲师。2011年毕业于英国爱丁堡大学，获得信息科学博士学位。主要研究方向包括自然语言处理、信息抽取以及机器学习在自然语言处理中的应用；已连续三年在面向结构化知识库的知识问答评测QALD-4, 5, 6中获得第一名；相关工作已发表在TPAMI、ACL、EMNLP等主流期刊与会议上。同时，作为项目负责人或课题骨干已承担多项国家自然科学基金及科技部863计划项目。分别在 2014 和 2015 年获得 IBM Faculty Award。 - 个人主页：https://sites.google.com/site/ysfeng/home
严睿，北京大学研究员，前百度公司资深研发，华中师范大学与中央财经大学客座教授与校外导师。主持研发多个开放领域对话系统和服务类对话系统，发表高水平研究论文近50篇，担任多个学术会议（KDD, SIGIR, ACL, WWW, AAAI, CIKM, EMNLP等）的（高级）程序委员会委员及审稿人。

[http://59.108.48.5/wip/team/teacher/zh/yanrui]

段楠博士，微软亚洲研究院自然语言计算组主管研究员，长期从事自动问答、对话系统、语义理解、文本生成和网络搜索等自然语言处理研究。段楠博士的多项研究成果已经转化到微软重要人工智能产品中，例如必应搜索、微软小冰、Cortana语音助手等。自2015年起，段楠博士开始担任NLPCC开放领域中文自动问答评测的负责人。 [https://www.microsoft.com/en-us/research/people/nanduan/]
高剑峰是微软合伙人，微软Redmond总部人工智能部门的研究经理（Partner Research Manager）。他致力于深度学习在文本和图像处理方面的研发，领导机器阅读理解、问答、对话方面的研究和人工智能系统开发，以及微软新一代商务人工智能系统的研发。6. 谢幸博士于2001年7月加入微软亚洲研究院，现任社会计算组高级主任研究员，并任中国科技大学兼职博士生导师。他分别于1996年和2001年在中国科技大学获得计算机软件专业学士和博士学位。目前，他的团队在数据挖掘、社会计算和普适计算等领域展开创新性的研究。 [https://www.microsoft.com/en-us/research/people/jfgao/]
谢幸，微软亚洲研究院，任社会计算组高级主任研究员，并任中国科技大学兼职博士生导师。目前，他的团队在数据挖掘、社会计算和普适计算等领域展开创新性的研究。他是ACM、IEEE高级会员和计算机学会杰出会员，多次担任顶级国际会议程序委员会委员和领域主席等职位。 [https://www.microsoft.com/en-us/research/people/xingx/]
Percy Liang 斯坦福大学计算机系助理教授、斯坦福人工智能实验室成员 [https://cs.stanford.edu/~pliang/\]
赵军博导中国科学院自动化研究所 http://people.ucas.ac.cn/~zhaojun
黄民烈清华大学http://www.tsinghua.edu.cn/publish/cs/4616/2013/20131122151220708543803/20131122151220708543803_.html