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Open-domain question answering aims at solving the task of locating the answers to user-generated questions in massive collections of documents.
Using tf-idf to determine word relevance in document queries
Ramos, J., et al · 2003
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The probabilistic relevance framework: Bm25 and beyond
Robertson, S., Zaragoza, H., et al · 2009
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Tagme: on-the-fly annotation of short text fragments (by wikipedia entities)
Ferragina, P., and Scaiella, U · 2010
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Fast and accurate annotation of short texts with wikipedia pages
Ferragina, P., and Scaiella, U · 2011
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Teaching machines to read and comprehend
Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P · 2015
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Evaluating search features of google knowledge graph and bing satori: entity types, list searches and query interfaces
Uyar, A., and Aliyu, F. M · 2015
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A thorough examination of the cnn/daily mail reading comprehension task
Chen, D., Bolton, J., and Manning, C. D · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Reading wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., and Bordes, A · 2017
Cited alongside, same era.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
Cited alongside, same era.
Machine comprehension by text-to-text neural question generation
Yuan, X., Wang, T., Gulcehre, C., Sordoni, A., Bachman, P., Subramanian, S., Zhang, S., and Trischler, A · 2017
Cited alongside, same era.
Neural question generation from text: A preliminary study
Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., and Zhou, M · 2017
Cited alongside, same era.
R 3: Reinforced ranker-reader for open-domain question answering
Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., and Jiang, J · 2018
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Question answering over knowledge graphs: question understanding via template decomposition
Zheng, W., Yu, J. X., Zou, L., and Cheng, H · 2018
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Multi-step retriever-reader interaction for scalable open-domain question answering
Das, R., Dhuliawala, S., Zaheer, M., and McCallum, A · 2019
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Evaluating rewards for question generation models
Hosking, T., and Riedel, S · 2019
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Knowledge graph embedding based question answering
Huang, X., Zhang, J., Li, D., and Li, P · 2019
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Cer, D., Yang, Y., Kong, S.-y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Training a ranking function for open-domain question answering
Htut, P. M., Bowman, S. R., and Cho, K · 2018
Cited alongside, same era.
Denoising distantly supervised open-domain question answering
Lin, Y., Ji, H., Liu, Z., and Sun, M · 2018
Cited alongside, same era.
Improving neural question generation using answer separation
Kim, Y., Lee, H., Shin, J., and Jung, K · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V · 2019
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Retrospective reader for machine reading comprehension
Zhang, Z., Yang, J., and Zhao, H · 2020
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