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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Original
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013 · 2013
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Diederik Kingma and Jimmy Ba. 2014 · 2014
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2017

Dynamic Fusion Networks for Machine Reading Comprehension

Xu, Yichong, Liu, Jingjing, Gao, Jianfeng et al.

Understand

This paper presents a novel neural model - Dynamic Fusion Network (DFN), for machine reading comprehension (MRC).

DFNs differ from most state-of-the-art models in their use of a dynamic multi-strategy attention process, in which passages, questions and answer candidates are jointly fused into attention vectors, along with a dynamic multi-step reasoning module for generating answers.
With the use of reinforcement learning, for each input sample that consists of a question, a passage and a list of candidate answers, an instance of DFN with a sample-specific network architecture can be dynamically constructed by determining what attention strategy to apply and how many reasoning steps to take.
Experiments show that DFNs achieve the best result reported on RACE, a challenging MRC dataset that contains real human reading questions in a wide variety of types.

Built on

Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Original
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Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Original
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Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
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Earlier work this paper cites.
Glove: Global vectors for word representation
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Earlier work this paper cites.
Teaching machines to read and comprehend
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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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