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Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulated in cloud APIs to serve over half a billion people worldwide and process over one hundred billion word generations per day.
WordNet: An electronic lexical database
Miller, G. A. 1998 · 1998
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Information hiding-a survey
Petitcolas, F. A.; Anderson, R. J.; and Kuhn, M. G. 1999 · 1999
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Bleu: a method for automatic evaluation of machine translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
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Mathematical statistics and data analysis
Rice, J. A. 2006 · 2006
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Moses: Open Source Toolkit for Statistical Machine Translation
Koehn, P.; Hoang, H.; Birch, A.; Callison-Burch, C.; Federico, M.; Bertoldi, N.; Cowan, B.; Shen, W.; Moran, C.; Zens, R.; Dyer, C.; Bojar, O.; Constantin, A.; and Herbst, E. 2007 · 2007
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Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation
Venugopal, A.; Uszkoreit, J.; Talbot, D.; Och, F.; and Ganitkevitch, J. 2011 · 2011
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Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
He, X.; Lyu, L.; Sun, L.; and Xu, Q. 2021a · 2012
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Parallel Data, Tools and Interfaces in OPUS
Tiedemann, J. 2012 · 2012
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Adding robustness to support vector machines against adversarial reverse engineering
Alabdulmohsin, I. M.; Gao, X.; and Zhang, X. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2014 · 2014
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Findings of the 2014 Workshop on Statistical Machine Translation
Bojar, O.; Buck, C.; Federmann, C.; Haddow, B.; Koehn, P.; Leveling, J.; Monz, C.; Pecina, P.; Post, M.; Saint-Amand, H.; Soricut, R.; Specia, L.; and Tamchyna, A. 2014 · 2014
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Report on the 11th iwslt evaluation campaign, iwslt 2014
Cettolo, M.; Niehues, J.; Stüker, S.; Bentivogli, L.; and Federico, M. 2014 · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Deep visual-semantic alignments for generating image descriptions
Karpathy, A.; and Fei-Fei, L. 2015 · 2015
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Spice: Semantic propositional image caption evaluation
Anderson, P.; Fernando, B.; Johnson, M.; and Gould, S. 2016 · 2016
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Neural Summarization by Extracting Sentences and Words
Cheng, J.; and Lapata, M. 2016 · 2016
Cited alongside, same era.
Abstractive sentence summarization with attentive recurrent neural networks
Chopra, S.; Auli, M.; and Rush, A. M. 2016 · 2016
Cited alongside, same era.
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Nallapati, R.; Zhou, B.; dos Santos, C.; glar Gulçehre, Ç.; and Xiang, B. 2016 · 2016
Cited alongside, same era.
Neural Machine Translation of Rare Words with Subword Units
Sennrich, R.; Haddow, B.; and Birch, A. 2016 · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
Tramèr, F.; Zhang, F.; Juels, A.; Reiter, M. K.; and Ristenpart, T. 2016 · 2016
Cited alongside, same era.
Six Challenges for Neural Machine Translation
Koehn, P.; and Knowles, R. 2017 · 2017
Cited alongside, same era.
PRADA: protecting against DNN model stealing attacks
Juuti, M.; Szyller, S.; Marchal, S.; and Asokan, N. 2019 · 2019
Later among the works it cites.
Defending against neural network model stealing attacks using deceptive perturbations
Lee, T.; Edwards, B.; Molloy, I.; and Su, D. 2019 · 2019
Later among the works it cites.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Ott, M.; Edunov, S.; Baevski, A.; Fan, A.; Gross, S.; Ng, N.; Grangier, D.; and Auli, M. 2019 · 2019
Later among the works it cites.
BERTScore: Evaluating Text Generation with BERT
Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2019 · 2019
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Thieves on Sesame Street! Model Extraction of BERT-based APIs
Krishna, K.; Tomar, G. S.; Parikh, A. P.; Papernot, N.; and Iyyer, M. 2020 · 2020
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Self-critical sequence training for image captioning
Rennie, S. J.; Marcheret, E.; Mroueh, Y.; Ross, J.; and Goel, V. 2017 · 2017
Cited alongside, same era.
Get To The Point: Summarization with Pointer-Generator Networks
See, A.; Liu, P. J.; and Manning, C. D. 2017 · 2017
Cited alongside, same era.
Embedding watermarks into deep neural networks
Uchida, Y.; Nagai, Y.; Sakazawa, S.; and Satoh, S. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Zhang, C.; Bengio, S.; Hardt, M.; Recht, B.; and Vinyals, O. 2017 · 2017
Cited alongside, same era.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Adi, Y.; Baum, C.; Cisse, M.; Pinkas, B.; and Keshet, J. 2018 · 2018
Cited alongside, same era.
Le Merrer, E.; Perez, P.; and Trédan, G. 2020 · 2020
Later among the works it cites.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
Later among the works it cites.
Protecting the Intellectual Property of Deep Neural Networks with Watermarking: The Frequency Domain Approach
Li, M.; Zhong, Q.; Zhang, L. Y.; Du, Y.; Zhang, J.; and Xiang, Y. 2020 · 2020
Later among the works it cites.
Multilingual Denoising Pre-training for Neural Machine Translation
Liu, Y.; Gu, J.; Goyal, N.; Li, X.; Edunov, S.; Ghazvininejad, M.; Lewis, M.; and Zettlemoyer, L. 2020 · 2020
Later among the works it cites.
Imitation Attacks and Defenses for Black-box Machine Translation Systems
Wallace, E.; Stern, M.; and Song, D. 2020 · 2020
Later among the works it cites.
Roundup Of Machine Learning Forecasts And Market Estimates For 2019
Columbus, L. 2019 · 2021
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Self-training Improves Pre-training for Natural Language Understanding
Du, J.; Grave, É.; Gunel, B.; Chaudhary, V.; Celebi, O.; Auli, M.; Stoyanov, V.; and Conneau, A. 2021 · 2021
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Dawn: Dynamic adversarial watermarking of neural networks
Szyller, S.; Atli, B. G.; Marchal, S.; and Asokan, N. 2021 · 2021
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Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs
Xu, Q.; He, X.; Lyu, L.; Qu, L.; and Haffari, G. 2021 · 2021
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Protect, show, attend and tell: Empowering image captioning models with ownership protection
Lim, J. H.; Chan, C. S.; Ng, K. W.; Fan, L.; and Yang, Q. 2022 · 2022
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Show, attend and tell: Neural image caption generation with visual attention
Xu, K.; Ba, J.; Kiros, R.; Cho, K.; Courville, A.; Salakhudinov, R.; Zemel, R.; and Bengio, Y. 2015 · 2057
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