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Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques.
Inequalities
G. H. Hardy, J. E. Littlewood, and G. Pólya · 1952
Earlier work this paper cites.
L. Wittgenstein · 1953
Earlier work this paper cites.
Building a question answering test collection
E. M. Voorhees and D. M. Tice · 2000
Earlier work this paper cites.
A Neural Probabilistic Language Model
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin · 2003
Earlier work this paper cites.
Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
B. Dolan, C. Quirk, and C. Brockett · 2004
Earlier work this paper cites.
Mining and summarizing customer reviews
M. Hu and B. Liu · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
B. Pang and L. Lee · 2004
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
B. Pang and L. Lee · 2005
Earlier work this paper cites.
Annotating expressions of opinions and emotions in language
J. Wiebe, T. Wilson, and C. Cardie · 2005
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Distributed representations of sentences and documents
Q. Le and T. Mikolov · 2014
Earlier work this paper cites.
Neural word embedding as implicit matrix factorization
O. Levy and Y. Goldberg · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Cited alongside, same era.
A sick cure for the evaluation of compositional distributional semantic models
M. Marelli, S. Menini, M. Baroni, L. Bentivogli, R. Bernardi, R. Zamparelli, et al · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
Cited alongside, same era.
A large annotated corpus for learning natural language inference
S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning · 2015
Cited alongside, same era.
Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia · 2017
Later among the works it cites.
Supervised learning of universal sentence representations from natural language inference data
A. Conneau, D. Kiela, H. Schwenk, L. Barrault, and A. Bordes · 2017
Later among the works it cites.
Allennlp: A deep semantic natural language processing platform
M. Gardner, J. Grus, M. Neumann, O. Tafjord, P. Dasigi, N. F. Liu, M. Peters, M. Schmitz, and L. S. Zettlemoyer · 2017
Later among the works it cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Later among the works it cites.
A survey of word embeddings evaluation methods
A. Bakarov · 2018
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Deep unordered composition rivals syntactic methods for text classification
M. Iyyer, V. Manjunatha, J. Boyd-Graber, and H. Daumé III · 2015
Cited alongside, same era.
Skip-thought vectors
R. Kiros, Y. Zhu, R. R. Salakhutdinov, R. Zemel, R. Urtasun, A. Torralba, and S. Fidler · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Learning distributed representations of sentences from unlabelled data
F. Hill, K. Cho, and A. Korhonen · 2016
Cited alongside, same era.
Bag of tricks for efficient text classification
A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov · 2016
Cited alongside, same era.
A simple but tough-to-beat baseline for sentence embeddings
S. Arora, Y. Liang, and T. Ma · 2017
Cited alongside, same era.
D. Cer, Y. Yang, S. Kong, N. Hua, N. Limtiaco, R. S. John, N. Constant, M. Guajardo-Cespedes, S. Yuan, C. Tar, Y. Sung, B. Strope, and R. Kurzweil · 2018
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Senteval: An evaluation toolkit for universal sentence representations
A. Conneau and D. Kiela · 2018
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties
A. Conneau, G. Kruszewski, G. Lample, L. Barrault, and M. Baroni · 2018
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Fine-tuned language models for text classification
J. Howard and S. Ruder · 2018
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Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
M. Pagliardini, P. Gupta, and M. Jaggi · 2018
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Deep contextualized word representations
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer · 2018
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Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018
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Concatenated p-mean word embeddings as universal cross-lingual sentence representations
A. Rücklé, S. Eger, M. Peyrard, and I. Gurevych · 2018
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