Fetching the paper…
Reading the bibliography…
Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks such as language modeling (Linzen et al., 2016) and neural machine translation (Shi et al., 2016).
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015a · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
The now-or-never bottleneck: A fundamental constraint on language
Morten H. Christiansen and Nick Chater. 2016 · 2016
Earlier work this paper cites.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Earlier work this paper cites.
Does string-based neural mt learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
Earlier work this paper cites.
Recurrent memory networks for language modeling
Ke Tran, Arianna Bisazza, and Christof Monz. 2016 · 2016
Earlier work this paper cites.
Sequence memory constraints give rise to language-like structure through iterated learning
Hannah Cornish, Rick Dale, Simon Kirby, and Morten H Christiansen. 2017 · 2017
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
Cited alongside, same era.
Tying word vectors and word classifiers: A loss framework for language modeling
Hakan Inan, Khashayar Khosravi, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Deep rnns encode soft hierarchical syntax
Terra Blevins, Omer Levy, and Luke Zettlemoyer. 2018 · 2018
Can neural networks understand logical entailment?
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette. 2018 · 2018
Closest in time.
Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
Closest in time.
Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni. 2018 · 2018
Closest in time.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Tree-structured composition in neural networks without tree-structured architectures
Samuel R. Bowman, Christopher D. Manning, and Christopher Potts. 2015b
Cited in the paper.