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Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning.
Learning to compose words into sentences with reinforcement learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2017 · 1906
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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Mathematical Methods in Linguistics , volume 30
Barbara BH Partee, Alice G ter Meulen, and Robert Wall. 2012 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Corpus-based induction of syntactic structure: Models of dependency and constituency
Dan Klein and Christopher Manning. 2014 · 2014
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Hybrid simplification using deep semantics and machine translation
Shashi Narayan and Claire Gardent. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015 · 2015
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Roee Aharoni and Yoav Goldberg. 2017 · 2017
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne. 2017 · 2017
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Categorical reparameterization with Gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
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Jointly learning sentence embeddings and syntax with unsupervised Tree-LSTMs
Jean Maillard, Stephen Clark, and Dani Yogatama. 2017 · 2017
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Coupling distributed and symbolic execution for natural language queries
Reinforcement learning from imperfect demonstrations
Yang Gao, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell, et al. 2018 · 2018
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Grammar induction with neural language models: An unusual replication
Phu Mon Htut, Kyunghyun Cho, and Samuel Bowman. 2018 · 2018
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Backpropagating through structured argmax using a SPIGOT
Hao Peng, Sam Thomson, and Noah A. Smith. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Neural language modeling by jointly learning syntax and lexicon
Yikang Shen, Zhouhan Lin, Chin-Wei Huang, and Aaron Courville. 2018 · 2018
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Lili Mou, Zhengdong Lu, Hang Li, and Zhi Jin. 2017 · 2017
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Learning to compose task-specific tree structures
Jihun Choi, Kang Min Yoo, and Sang-goo Lee. 2018 · 2018
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Latent alignment and variational attention
Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, and Alexander Rush. 2018 · 2018
Cited alongside, same era.
Dynamic compositionality in recursive neural networks with structure-aware tag representations
Taeuk Kim, Jihun Choi, Daniel Edmiston, Sanghwan Bae, and Sang-goo Lee. 2019a
Cited in the paper.
Unsupervised recurrent neural network grammars
Yoon Kim, Alexander M Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, and Gábor Melis. 2019b
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Do latent tree learning models identify meaningful structure in sentences?
Adina Williams, Andrew Drozdov, and Samuel R. Bowman. 2018a
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2018b
Cited in the paper.
Haoyue Shi, Hao Zhou, Jiaze Chen, and Lei Li. 2018 · 2018
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Unsupervised latent tree induction with deep inside-outside recursive autoencoders
Andrew Drozdov, Pat Verga, Mohit Yadav, Mohit Iyyer, and Andrew McCallum. 2019 · 2019
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Ordered neurons: Integrating tree structures into recurrent neural networks
Yikang Shen, Shawn Tan, Alessandro Sordoni, and Aaron Courville. 2019 · 2019
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