Fetching the paper…
Reading the bibliography…
We describe an approach for unsupervised learning of a generic, distributed sentence encoder.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Learning question classifiers
Xin Li and Dan Roth · 2002
Earlier work this paper cites.
Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
Bill Dolan, Chris Quirk, and Chris Brockett · 2004
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee · 2004
Earlier work this paper cites.
Using machine translation evaluation techniques to determine sentence-level semantic equivalence
Andrew Finch, Young-Sook Hwang, and Eiichiro Sumita · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
Earlier work this paper cites.
Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie · 2005
Earlier work this paper cites.
Using dependency-based features to take the “para-farce” out of paraphrase
Stephen Wan, Mark Dras, Robert Dale, and Cécile Paris · 2006
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Paraphrase identification as probabilistic quasi-synchronous recognition
Dipanjan Das and Noah A Smith · 2009
Earlier work this paper cites.
Dynamic pooling and unfolding recursive autoencoders for paraphrase detection
Richard Socher, Eric H Huang, Jeffrey Pennin, Christopher D Manning, and Andrew Y Ng · 2011
Earlier work this paper cites.
Re-examining machine translation metrics for paraphrase identification
Nitin Madnani, Joel Tetreault, and Martin Chodorow · 2012
Earlier work this paper cites.
Baselines and bigrams: Simple, good sentiment and topic classification
Sida Wang and Christopher D Manning · 2012
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
Cited alongside, same era.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Cited alongside, same era.
Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom · 2013
Cited alongside, same era.
Exploiting similarities among languages for machine translation
Tomas Mikolov, Quoc V Le, and Ilya Sutskever · 2013
Cited alongside, same era.
A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
Cited alongside, same era.
Convolutional neural networks for sentence classification
The meaning factory: Formal semantics for recognizing textual entailment and determining semantic similarity
Johannes Bjerva, Johan Bos, Rob van der Goot, and Malvina Nissim · 2014
Later among the works it cites.
Ecnu: One stone two birds: Ensemble of heterogenous measures for semantic relatedness and textual entailment
Jiang Zhao, Tian Tian Zhu, and Man Lan · 2014
Later among the works it cites.
Grounded compositional semantics for finding and describing images with sentences
Richard Socher, Andrej Karpathy, Quoc V Le, Christopher D Manning, and Andrew Y Ng · 2014
Later among the works it cites.
Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment
Marco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini, and Roberto Zamparelli · 2014
Later among the works it cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yoon Kim · 2014
Cited alongside, same era.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Cited alongside, same era.
Distributed representations of sentences and documents
Quoc V Le and Tomas Mikolov · 2014
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2014
Cited alongside, same era.
Later among the works it cites.
Self-adaptive hierarchical sentence model
Han Zhao, Zhengdong Lu, and Pascal Poupart · 2015
Closest in time.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Closest in time.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Closest in time.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Closest in time.
Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
Closest in time.
Deep visual-semantic alignments for generating image descriptions
A. Karpathy and L. Fei-Fei · 2015
Closest in time.
Associating neural word embeddings with deep image representations using fisher vectors
Benjamin Klein, Guy Lev, Gil Sadeh, and Lior Wolf · 2015
Closest in time.
Deep captioning with multimodal recurrent neural networks (m-rnn)
Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan Yuille · 2015
Closest in time.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Closest in time.