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Sentence-level representations are beneficial for various natural language processing tasks.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Recursive deep models for semantic compositionality over a sentiment treebank
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Empirical evaluation of gated recurrent neural networks on sequence modeling
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Skip-thought vectors
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Findings of the 2016 conference on machine translation
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Learning distributed representations of sentences from unlabelled data
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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Learning generic sentence representations using convolutional neural networks
Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He, and Lawrence Carin. 2017 · 2017
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Categorical reparameterization with gumbel-softmax
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Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. 2017 · 2017
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MultiWOZ - a large-scale multi-domain Wizard-of-Oz dataset for task-oriented dialogue modelling
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SentEval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Loss functions for multiset prediction
The curious case of neural text degeneration
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Privacy risks of general-purpose language models
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Stanza: A python natural language processing toolkit for many human languages
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Information leakage in embedding models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
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Mpnet: Masked and permuted pre-training for language understanding
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Learning universal sentence representations with mean-max attention autoencoder
Minghua Zhang, Yunfang Wu, Weikang Li, and Wei Li. 2018a · 2018
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Personalizing dialogue agents: I have a dog, do you have pets too?
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Language models are unsupervised multitask learners
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Extracting training data from large language models
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SimCSE: Simple contrastive learning of sentence embeddings
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Understanding unintended memorization in language models under federated learning
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Recovering private text in federated learning of language models
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
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Opt: Open pre-trained transformer language models
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