Fetching the paper…
Reading the bibliography…
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.
Fast and accurate annotation of short texts with wikipedia pages
Paolo Ferragina and Ugo Scaiella · 2011
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
Earlier work this paper cites.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
Earlier work this paper cites.
Tying word vectors and word classifiers: A loss framework for language modeling
Hakan Inan, Khashayar Khosravi, and Richard Socher · 2017
Earlier work this paper cites.
Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2017
Earlier work this paper cites.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
Earlier work this paper cites.
Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher · 2017
Earlier work this paper cites.
Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner · 2018
Earlier work this paper cites.
Adaptive document retrieval for deep question answering
Bernhard Kratzwald and Stefan Feuerriegel · 2018
Earlier work this paper cites.
Ranking paragraphs for improving answer recall in open-domain question answering
Jinhyuk Lee, Seongjun Yun, Hyunjae Kim, Miyoung Ko, and Jaewoo Kang · 2018
Earlier work this paper cites.
Denoising distantly supervised open-domain question answering
Yankai Lin, Haozhe Ji, Zhiyuan Liu, and Maosong Sun · 2018
Cited alongside, same era.
Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong · 2018
Cited alongside, same era.
Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli · 2018
Cited alongside, same era.
Weaver: Deep co-encoding of questions and documents for machine reading
Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, and Antoine Bordes · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
Cited alongside, same era.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Rhinehart, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, et al · 2019
Closest in time.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova · 2019
Closest in time.
RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Closest in time.
Revealing the importance of semantic retrieval for machine reading at scale
Yixin Nie, Songhe Wang, and Mohit Bansal · 2019
Closest in time.
Answering while summarizing: Multi-task learning for multi-hop qa with evidence extraction
Kosuke Nishida, Kyosuke Nishida, Nagata Masaaki, Atsushi Otsuka, Itsumi Saito, Hisako Asano, and Junji Tomita · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
Cited alongside, same era.
Multi-step retriever-reader interaction for scalable open-domain question answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang · 2019
Cited alongside, same era.
Multi-hop paragraph retrieval for open-domain question answering
Yair Feldman and Ran El-Yaniv · 2019
Cited alongside, same era.
Multi-step entity-centric information retrieval for multi-hop question answering
Ameya Godbole, Dilip Kavarthapu, Rajarshi Das, Zhiyu Gong, Abhishek Singhal, Xiaoxiao Yu, Mo Guo, Tian Gao, Hamed Zamani, Manzil Zaheer, and Andrew McCallum · 2019
Cited alongside, same era.
Retrieve, read, rerank: Towards end-to-end multi-document reading comprehension
Minghao Hu, Yuxing Peng, Zhen Huang, and Dongsheng Li · 2019
Cited alongside, same era.
Rodrigo Nogueira and Kyunghyun Cho · 2019
Closest in time.
Answering complex open-domain questions through iterative query generation
Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, and Christopher D. Manning · 2019
Closest in time.
Real-time open-domain question answering with dense-sparse phrase index
Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur P Parikh, Ali Farhadi, and Hannaneh Hajishirzi · 2019
Closest in time.
PullNet: Open domain question answering with iterative retrieval on knowledge bases and text
Haitian Sun, Tania Bedrax-Weiss, and William Cohen · 2019
Closest in time.
Dynamically fused graph network for multi-hop reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu · 2019
Closest in time.
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
Closest in time.
Transformer-XH: Multi-hop question answering with extra hop attention
Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul Bennett, and Saurabh Tiwary · 2020
Closest in time.