Fetching the paper…
Reading the bibliography…
Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level.
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.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al. 1998 · 1998
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. 2000 · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
A systematic comparison of various statistical alignment models
Franz Josef Och and Hermann Ney. 2003 · 2003
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Joint learning of a dual smt system for paraphrase generation
Hong Sun and Ming Zhou. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
On using monolingual corpora in neural machine translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Recurrent hidden semi-markov model
Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, and Le Song. 2016 · 2016
Cited alongside, same era.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 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 · 2016
Cited alongside, same era.
Joint copying and restricted generation for paraphrase
Ziqiang Cao, Chuwei Luo, Wenjie Li, and Sujian Li. 2017 · 2017
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Later among the works it cites.
Cross-domain semantic parsing via paraphrasing
Yu Su and Xifeng Yan. 2017 · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Instance weighting for neural machine translation domain adaptation
Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, and Eiichiro Sumita. 2017b · 2017
Later among the works it cites.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Paraphrase generation with deep reinforcement learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Using target-side monolingual data for neural machine translation through multi-task learning
Tobias Domhan and Felix Hieber. 2017 · 2017
Cited alongside, same era.
A deep generative framework for paraphrase generation
Ankush Gupta, Arvind Agarwal, Prawaan Singh, and Piyush Rai. 2017 · 2017
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
Cited alongside, same era.
Deep recurrent generative decoder for abstractive text summarization
Piji Li, Wai Lam, Lidong Bing, and Zihao Wang. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Cited alongside, same era.
Sentence embedding for neural machine translation domain adaptation
Rui Wang, Andrew Finch, Masao Utiyama, and Eiichiro Sumita. 2017a
Cited in the paper.
Zichao Li, Xin Jiang, Lifeng Shang, and Hang Li. 2018 · 2018
Later among the works it cites.
Quase: Sequence editing under quantifiable guidance
Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, and Tong Zhang. 2018 · 2018
Later among the works it cites.
Query and output: Generating words by querying distributed word representations for paraphrase generation
Shuming Ma, Xu Sun, Wei Li, Sujian Li, Wenjie Li, and Xuancheng Ren. 2018 · 2018
Later among the works it cites.
A task in a suit and a tie: paraphrase generation with semantic augmentation
Su Wang, Rahul Gupta, Nancy Chang, and Jason Baldridge. 2018 · 2018
Later among the works it cites.
Learning neural templates for text generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2018 · 2018
Later among the works it cites.