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Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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A deep look into neural ranking models for information retrieval
Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, and Xueqi Cheng. 2019 · 1903
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Investigating the successes and failures of BERT for passage re-ranking
Harshith Padigela, Hamed Zamani, and W. Bruce Croft. 2019 · 1905
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Relevance feedback in information retrieval
J. J. Rocchio. 1971 · 1971
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Quary expansion using local and global document analysis
Jinxi Xu and W Bruce Croft. 1996 · 1996
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Relevance-based language models
Victor Lavrenko and W Bruce Croft. 2001 · 2001
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A study of smoothing methods for language models applied to ad hoc information retrieval
Chengxiang Zhai and John Lafferty. 2001 · 2001
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Large-scale named entity disambiguation based on wikipedia data
Silviu Cucerzan. 2007 · 2007
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Selecting good expansion terms for pseudo-relevance feedback
Guihong Cao, Jian-Yun Nie, Jianfeng Gao, and Stephen Robertson. 2008 · 2008
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Knowledge base population: Successful approaches and challenges
Heng Ji and Ralph Grishman. 2011 · 2011
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Entity query feature expansion using knowledge base links
Jeffrey Dalton, Laura Dietz, and James Allan. 2014 · 2014
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Umass at trec web 2014: Entity query feature expansion using knowledge base links
Laura Dietz and Patrick Verga. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Xitong Liu and Hui Fang. 2015 · 2015
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Ask the right questions: Active question reformulation with reinforcement learning
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Task-oriented query reformulation with reinforcement learning
Rodrigo Nogueira and Kyunghyun Cho. 2017 · 2017
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Word-entity duet representations for document ranking
Chenyan Xiong, Jamie Callan, and Tie-Yan Liu. 2017 · 2017
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Jonathan Raphael Raiman and Olivier Michel Raiman. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Multi-step retriever-reader interaction for scalable open-domain question answering
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