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Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences.
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Robert Jacobs, Michael Jordan, Steven J. Nowlan, and Geoffrey E. Hinton. 1991 · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams. 1992 · 1992
Earlier work this paper cites.
A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants
Radford M. Neal and Geoffrey E. Hinton. 1998 · 1998
Earlier work this paper cites.
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Ehud Reiter and Robert Dale. 2000 · 2000
Earlier work this paper cites.
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Kishore Papineni, Salim Roukos, Todd Ward, and Wj Wei-jing Zhu. 2002 · 2002
Earlier work this paper cites.
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Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Earlier work this paper cites.
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Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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