Reading Wikipedia to Answer Open-Domain Questions

Keywords

Computer science, Question answering, Bigram, Information retrieval, Task (project management)

References

• Mining Knowledge fromWikipedia for the Question Answering taskDavide Buscaldi, Paolo Rosso — 2006
• Large-scale Simple Question Answering with Memory NetworksAntoine Bordes, Nicolas Usunier, Sumit Chopra — 2015
• Teaching Machines to Read and ComprehendKarl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette — 2015
• Open Domain Question Answering via Semantic EnrichmentHuan Sun, Hao Ma, Wen-tau Yih — 2015
• Open domain question answering using Wikipedia-based knowledge modelPum-Mo Ryu, Myung-Gil Jang, Hyunki Kim — 2014
• Feature hashing for large scale multitask learningKilian Q. Weinberger, Anirban Dasgupta, John Langford — 2009
• Open question answering over curated and extracted knowledge basesAnthony Fader, Luke Zettlemoyer, Oren Etzioni — 2014
• Freebase: a collaboratively created graph database for structuring human knowledgeKurt Bollacker, Colin Evans, Praveen Paritosh — 2008
• Distant supervision for relation extraction without labeled dataMike D. Mintz, Steven Bills, Rion Snow — 2009
• An Analysis of the AskMSR Question-Answering SystemEric Brill, Susan Dumais, Michele Banko — 2002
• A unified architecture for natural language processing: deep neural networks with multitask learningRonan Collobert, Jason Weston — 2008
• The Stanford CoreNLP Natural Language Processing ToolkitChristopher D. Manning, Mihai Surdeanu, John Bauer — 2014
• The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsFelix Hill, Antoine Bordes, Sumit Chopra — 2015
• A Neural Network for Factoid Question Answering over ParagraphsMohit Iyyer, Jordan Boyd‐Graber, Leonardo Claudino — 2014
• Building Watson: An Overview of the DeepQA ProjectDavid Ferrucci, Eric W. Brown, Jennifer Chu‐Carroll — 2010
• GloVe: Global Vectors for Word RepresentationJeffrey Pennington, Richard Socher, Christopher D. Manning — 2014
• Semantic Parsing on Freebase from Question-Answer PairsJonathan Berant, Andrew Chou, Roy Frostig — 2013
• Key-Value Memory Networks for Directly Reading DocumentsAlexander Miller, Adam Fisch, Jesse Dodge — 2016
• What makes ImageNet good for transfer learning?Minyoung Huh, Pulkit Agrawal, Alexei A. Efros — 2016
• WikiReading: A Novel Large-scale Language Understanding Task over WikipediaDaniel Hewlett, Alexandre Lacoste, Llion Jones — 2016

Cited by

• ReasoNet: Learning to Stop Reading in Machine ComprehensionYelong Shen, Po-Sen Huang, Jianfeng Gao — 2016
• Ruminating Reader: Reasoning with Gated Multi-hop AttentionYichen Gong, Samuel R. Bowman — 2017
• Mnemonic Reader for Machine ComprehensionMinghao Hu, Yuxing Peng, Xipeng Qiu — 2017
• ParlAI: A Dialog Research Software PlatformAlexander Miller, Will Feng, Dhruv Batra — 2017
• Reading Twice for Natural Language UnderstandingDirk Weissenborn — 2017
• Making Neural QA as Simple as Possible but not SimplerDirk Weissenborn, Georg Wiese, Laura Seiffe — 2017
• Quasar: Datasets for Question Answering by Search and ReadingBhuwan Dhingra, Kathryn Mazaitis, William W. Cohen — 2017
• Evaluating Semantic Parsing against a Simple Web-based Question Answering ModelAlon Talmor, Mor Geva, Jonathan Berant — 2017
• Reinforced Mnemonic Reader for Machine Reading ComprehensionMinghao Hu, Yuxing Peng, Xipeng Qiu — 2017
• R3: Reinforced Reader-Ranker for Open-Domain Question AnsweringShuohang Wang, Mo Yu, Xiaoxiao Guo — 2017
• A Unified Query-based Generative Model for Question Generation and Question AnsweringLinfeng Song, Zhiguo Wang, Wael Hamza — 2017
• Natural Language Inference over Interaction SpaceYichen Gong, Heng Luo, Jian Zhang — 2017
• Smarnet: Teaching Machines to Read and Comprehend Like HumanZheqian Chen, Rongqin Yang, Bin Cao — 2017
• Multi-Mention Learning for Reading Comprehension with Neural CascadesSwabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski — 2017
• Simple and Effective Multi-Paragraph Reading ComprehensionChristopher Clark, Matt Gardner — 2017
• Phase Conductor on Multi-layered Attentions for Machine ComprehensionRui Liu, Wei Wei, Weiguang Mao — 2017
• Keyword-based Query Comprehending via Multiple Optimized-Demand AugmentationBoyuan Pan, Hao Li, Zhou Zhao — 2017
• DCN+: Mixed Objective and Deep Residual Coattention for Question AnsweringCaiming Xiong, Victor W. Zhong, Richard Socher — 2017
• Neural Skill Transfer from Supervised Language Tasks to Reading ComprehensionTodor Mihaylov, Zornitsa Kozareva, Anette Frank — 2017
• Dynamic Fusion Networks for Machine Reading ComprehensionYichong Xu, Jun Liu, Jianfeng Gao — 2017

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