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Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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Fusionnet: Fusing via fully-aware attention with application to machine comprehension
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, and Weizhu Chen · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning · 2017
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Quac: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
A dataset and baselines for sequential open-domain question answering
Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber · 2018
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Flowqa: Grasping flow in history for conversational machine comprehension
Hsin-Yuan Huang, Eunsol Choi, and Wen-tau Yih · 2018
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2018
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A qualitative comparison of coqa, squad 2.0 and quac
Mark Yatskar · 2018
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.