Fetching the paper…
Reading the bibliography…
We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering.
Aspects of the Theory of Syntax
Noam Chomsky · 1965
Earlier work this paper cites.
Function optimization using connectionist reinforcement learning algorithms
Ronald J. Williams and Jing Peng · 1991
Earlier work this paper cites.
Introduction to Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1998
Earlier work this paper cites.
Modern Information Retrieval
Ricardo Baeza-Yates and Berthier Ribeiro-Neto · 1999
Earlier work this paper cites.
Discovery of inference rules for question-answering
Dekang Lin and Patrick Pantel · 2001
Earlier work this paper cites.
Predicting query performance
Steve Cronen-Townsend, Yun Zhou, and W. Bruce Croft · 2002
Earlier work this paper cites.
Variance reduction techniques for gradient estimates in reinforcement learning
Evan Greensmith, Peter L Bartlett, and Jonathan Baxter · 2004
Earlier work this paper cites.
Answering the question you wish they had asked: The impact of paraphrasing for question answering
Pablo Ariel Duboue and Jennifer Chu-Carroll · 2006
Earlier work this paper cites.
Exploratory search: From finding to understanding
Gary Marchionini · 2006
Earlier work this paper cites.
Statistical machine translation for query expansion in answer retrieval
Stefan Riezler, Alexander Vasserman, Ioannis Tsochantaridis, Vibhu Mittal, and Yi Liu · 2007
Earlier work this paper cites.
Reducing long queries using query quality predictors
Giridhar Kumaran and Vitor R. Carvalho · 2009
Earlier work this paper cites.
Better hypothesis testing for statistical machine translation: Controlling for optimizer instability
Jonathan H. Clark, Chris Dyer, Alon Lavie, and Noah A. Smith · 2011
Earlier work this paper cites.
Paraphrase-Driven Learning for Open Question Answering
Anthony Fader, Luke Zettlemoyer, and Oren Etzioni · 2013
Cited alongside, same era.
Semantic parsing via paraphrasing
Jonathan Berant and Percy Liang · 2014
Cited alongside, same era.
Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Cited alongside, same era.
Language understanding for text-based games using deep reinforcement learning
Karthik Narasimhan, Tejas Kulkarni, and Regina Barzilay · 2015
Cited alongside, same era.
Dual learning for machine translation
Yingce Xia, Di He, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma · 2016
Later among the works it cites.
The united nations parallel corpus v1.0
Michał Ziemski, Marcin Junczys-Dowmunt, and Bruno Poliquen · 2016
Later among the works it cites.
Massive exploration of neural machine translation architectures
Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc Le · 2017
Closest in time.
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
Matthew Dunn, Levent Sagun, Mike Higgins, Ugur Guney, Volkan Cirik, and Kyunghyun Cho · 2017
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2015
Cited alongside, same era.
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
Cited alongside, same era.
Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky · 2016
Cited alongside, same era.
End-to-end goal-driven web navigation
Rodrigo Nogueira and Kyunghyun Cho · 2016
Cited alongside, same era.
Neural paraphrase generation with stacked residual LSTM networks
Aaditya Prakash, Sadid A. Hasan, Kathy Lee, Vivek Datla, Ashequl Qadir, Joey Liu, and Oladimeji Farri · 2016
Cited alongside, same era.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Cited alongside, same era.
Bidirectional Attention Flow for Machine Comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi
Cited in the paper.
Deal or no deal? end-to-end learning of negotiation dialogues
Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, and Dhruv Batra · 2017
Closest in time.
Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao · 2017
Closest in time.
Paraphrasing revisited with neural machine translation
Jonathan Mallinson, Rico Sennrich, and Mirella Lapata · 2017
Closest in time.
Task-oriented query reformulation with reinforcement learning
Rodrigo Nogueira and Kyunghyun Cho · 2017
Closest in time.
Query-reduction networks for question answering
Minjoon Seo, Sewon Min, Ali Farhadi, and Hannaneh Hajishirzi · 2017
Closest in time.
Computational fact checking through query perturbations
You Wu, Pankaj K. Agarwal, Chengkai Li, Jun Yang, and Cong Yu · 2017
Closest in time.