Fetching the paper…
Reading the bibliography…
Beam search is the most widely used decoding method for neural machine translation (NMT).
The backtranslation score: Automatic mt evalution at the sentence level without reference translations
Reinhard Rapp · 2009
Earlier work this paper cites.
Efficient elicitation of annotations for human evaluation of machine translation
Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
A simple, fast diverse decoding algorithm for neural generation
Jiwei Li, Will Monroe, and Dan Jurafsky · 2016
Earlier work this paper cites.
Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K. Vijayakumar, Michael Cogswell, Ramprasaath R. Selvaraju, Qing Sun, Stefan Lee, David J. Crandall, and Dhruv Batra · 2016
Earlier work this paper cites.
Exploring hypotheses spaces in neural machine translation
Frédéric Blain, Lucia Specia, and Pranava Madhyastha · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Later-stage minimum bayes-risk decoding for neural machine translation
Raphael Shu and Hideki Nakayama · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann N. Dauphin · 2018
Cited alongside, same era.
Understanding measures of uncertainty for adversarial example detection
Lewis Smith and Yarin Gal · 2018
Cited alongside, same era.
Facebook fair’s WMT19 news translation task submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Improving back-translation with uncertainty-based confidence estimation
Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, and Maosong Sun · 2019
Cited alongside, same era.
Leveraging sentence similarity in natural language generation: Improving beam search using range voting
Sebastian Borgeaud and Guy Emerson · 2020
Transquest: Translation quality estimation with cross-lingual transformers
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov · 2020
Later among the works it cites.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C. Farinha, and Alon Lavie · 2020
Later among the works it cites.
BLEURT: learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P. Parikh · 2020
Later among the works it cites.
Energy-based reranking: Improving neural machine translation using energy-based models
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum · 2021
Later among the works it cites.
Sampling-based minimum bayes risk decoding for neural machine translation
Bryan Eikema and Wilker Aziz · 2021
Later among the works it cites.
A theoretical analysis of the repetition problem in text generation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Is MAP decoding all you need? the inadequacy of the mode in neural machine translation
Bryan Eikema and Wilker Aziz · 2020
Cited alongside, same era.
Unsupervised quality estimation for neural machine translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Cited alongside, same era.
If beam search is the answer, what was the question?
Clara Meister, Ryan Cotterell, and Tim Vieira · 2020
Cited alongside, same era.
Zihao Fu, Wai Lam, Anthony Man-Cho So, and Bei Shi · 2021
Later among the works it cites.
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2021
Later among the works it cites.
Machine translation decoding beyond beam search
Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Jean-Baptiste Lespiau, Ioannis Antonoglou, Karen Simonyan, and Oriol Vinyals · 2021
Later among the works it cites.
Discriminative reranking for neural machine translation
Ann Lee, Michael Auli, and Marc’Aurelio Ranzato · 2021
Later among the works it cites.