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We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps.
Okapi at TREC-3
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Leveraging passage retrieval with generative models for open domain question answering
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Relevance-guided supervision for OpenQA with ColBERT
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Sparta: Efficient open-domain question answering via sparse transformer matching retrieval
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When documents are very long, BM25 fails!
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HopRetriever: Retrieve hops over wikipedia to answer complex questions
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The Stanford CoreNLP natural language processing toolkit
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WikiExtractor
Giusepppe Attardi. 2015 · 2015
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Elasticsearch: The definitive guide: A distributed real-time search and analytics engine
Clinton Gormley and Zachary Tong. 2015 · 2015
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On using very large target vocabulary for neural machine translation
Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015 · 2015
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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SearchQA: A new Q&A dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Knowledge-aided open-domain question answering
Mantong Zhou, Zhouxing Shi, Minlie Huang, and Xiaoyan Zhu. 2020 · 2017
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019
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Revealing the importance of semantic retrieval for machine reading at scale
Yixin Nie, Songhe Wang, and Mohit Bansal. 2019 · 2019
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Answering complex open-domain questions through iterative query generation
Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, and Christopher D. Manning. 2019 · 2019
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Multi-passage BERT: A globally normalized BERT model for open-domain question answering
Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019 · 2019
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End-to-end open-domain question answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 2019
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Simple and effective multi-paragraph reading comprehension
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Know what you don’t know: Unanswerable questions for SQuAD
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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 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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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul Bennett, and Saurabh Tiwary. 2019 · 2019
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Learning to retrieve reasoning paths over Wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020 · 2020
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Answering complex open-domain questions with multi-hop dense retrieval
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IDRQA: Iterative document reranking for open-domain multi-hop question answering
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