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Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask.
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Fast exact inference with a factored model for natural language parsing
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Learning to rank using gradient descent
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Discriminative reranking for natural language parsing
Michael Collins and Terry Koo. 2005 · 2005
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Questionbank: Creating a corpus of parse-annotated questions
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Using dependency-based features to take the’para-farce’out of paraphrase
Stephen Wan, Mark Dras, Robert Dale, and Cécile Paris. 2006 · 2006
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Simplenlg: A realisation engine for practical applications
Albert Gatt and Ehud Reiter. 2009 · 2009
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News from opus-a collection of multilingual parallel corpora with tools and interfaces
Jörg Tiedemann. 2009 · 2009
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Good question! statistical ranking for question generation
Michael Heilman and Noah A. Smith. 2010 · 2010
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The first question generation shared task evaluation challenge
Vasile Rus, Brendan Wyse, Paul Piwek, Mihai Lintean, Svetlana Stoyanchev, and Cristian Moldovan. 2010 · 2010
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Semantics-based question generation and implementation
Xuchen Yao, Gosse Bouma, and Yi Zhang. 2012 · 2012
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Deep questions without deep understanding
Igor Labutov, Sumit Basu, and Lucy Vanderwende. 2015 · 2015
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Learning to rank short text pairs with convolutional deep neural networks
Aliaksei Severyn and Alessandro Moschitti. 2015 · 2015
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Ms marco: A human generated machine reading comprehension dataset
Transforming question answering datasets into natural language inference datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018 · 2018
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018 · 2018
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A dataset and baselines for sequential open-domain question answering
Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. 2018 · 2018
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Natural answer generation with heterogeneous memory
Yao Fu and Yansong Feng. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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A decomposable attention model for natural language inference
Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
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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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Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus
Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron Courville, and Yoshua Bengio. 2016 · 2016
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Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Cited alongside, same era.
Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
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OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph
Amrita Saha, Vardaan Pahuja, Mitesh M Khapra, Karthik Sankaranarayanan, and Sarath Chandar. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
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A qualitative comparison of CoQA, SQuAD 2.0 and QuAC
Mark Yatskar. 2019 · 2019
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Dialogpt: Large-scale generative pre-training for conversational response generation
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2019 · 2019
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Identifying where to focus in reading comprehension for neural question generation
Xinya Du and Claire Cardie. 2017 · 2073
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