Fetching the paper…
Reading the bibliography…
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models.
Mathematical statistics: Basic ideas and selected topics
Peter J Bickel and Kjell A Doksum. 1977 · 1977
Earlier work this paper cites.
Statistical decision theory and Bayesian analysis; 2nd ed
James O Berger. 1985 · 1985
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Confidence estimation for machine translation
John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2004 · 2004
Earlier work this paper cites.
Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
Discriminative reranking for machine translation
Libin Shen, Anoop Sarkar, and Franz Josef Och. 2004 · 2004
Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Sequence transduction with recurrent neural networks
Alex Graves. 2012 · 2012
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
chrF: character n-gram F-score for automatic MT evaluation
Maja Popović. 2015 · 2015
Earlier work this paper cites.
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda B. Vi’egas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Earlier work this paper cites.
Controlling politeness in neural machine translation via side constraints
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Controlling the voice of a sentence in Japanese-to-English neural machine translation
Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, and Mamoru Komachi. 2016 · 2016
Earlier work this paper cites.
Domain control for neural machine translation
Catherine Kobus, Josep Crego, and Jean Senellart. 2017 · 2017
Earlier work this paper cites.
Analyzing uncertainty in neural machine translation
Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018 · 2018
Earlier work this paper cites.
Getting gender right in neural machine translation
Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2018 · 2018
Earlier work this paper cites.
Controlling text complexity in neural machine translation
Sweta Agrawal and Marine Carpuat. 2019 · 2019
Cited alongside, same era.
Tagged back-translation
Isaac Caswell, Ciprian Chelba, and David Grangier. 2019 · 2019
Cited alongside, same era.
OpenKiwi: An open source framework for quality estimation
Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, and André F. T. Martins. 2019 · 2019
Cited alongside, same era.
Controlling the reading level of machine translation output
Kelly Marchisio, Jialiang Guo, Cheng-I Lai, and Philipp Koehn. 2019 · 2019
Cited alongside, same era.
Generating diverse translations with sentence codes
Raphael Shu, Hideki Nakayama, and Kyunghyun Cho. 2019 · 2019
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 · 2020
Cited alongside, same era.
Identifying weaknesses in machine translation metrics through minimum Bayes risk decoding: A case study for COMET
Chantal Amrhein and Rico Sennrich. 2022 · 2022
Later among the works it cites.
Sampling-based approximations to minimum Bayes risk decoding for neural machine translation
Bryan Eikema and Wilker Aziz. 2022 · 2022
Later among the works it cites.
Quality-aware decoding for neural machine translation
Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins. 2022 · 2022
Later among the works it cites.
A natural diet: Towards improving naturalness of machine translation output
Markus Freitag, David Vilar, David Grangier, Colin Cherry, and George Foster. 2022c · 2022
Later among the works it cites.
Truncation sampling as language model desmoothing
John Hewitt, Christopher Manning, and Percy Liang. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
BLEU might be guilty but references are not innocent
Markus Freitag, David Grangier, and Isaac Caswell. 2020 · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Tangled up in BLEU: Reevaluating the evaluation of automatic machine translation evaluation metrics
Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2020 · 2020
Cited alongside, same era.
TransQuest: Translation quality estimation with cross-lingual transformers
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2020 · 2020
Cited alongside, same era.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020a · 2020
Cited alongside, same era.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
Cited alongside, same era.
Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, and Maja Popović. 2022 · 2022
Later among the works it cites.
Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vlad Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, Andrea Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wenhao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Music Li, Wei Li, YaGuang Li, Jian Li, Hyeontaek Lim, Hanzhao Lin, Zhongtao Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alex Castro Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniel Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Kelvin Xu, Yunhan Xu, Linting Xue, Pengcheng Yin, Jiahui Yu, Qiao Zhang, Steven Zheng, Ce Zheng, Weikang Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. 2023 · 2023
Closest in time.
Findings of the WMT 2023 shared task on quality estimation
Frederic Blain, Chrysoula Zerva, Ricardo Ribeiro, Nuno M. Guerreiro, Diptesh Kanojia, José G. C. de Souza, Beatriz Silva, Tânia Vaz, Yan Jingxuan, Fatemeh Azadi, Constantin Orasan, and André Martins. 2023 · 2023
Closest in time.
Faster minimum bayes risk decoding with confidence-based pruning
Julius Cheng and Andreas Vlachos. 2023 · 2023
Closest in time.
MetricX-23: The Google submission to the WMT 2023 metrics shared task
Juraj Juraska, Mara Finkelstein, Daniel Deutsch, Aditya Siddhant, Mehdi Mirzazadeh, and Markus Freitag. 2023 · 2023
Closest in time.
Beyond correlation: Making sense of the score differences of new MT evaluation metrics
Chi-kiu Lo, Rebecca Knowles, and Cyril Goutte. 2023 · 2023
Closest in time.
There’s no data like better data: Using QE metrics for MT data filtering
Jan-Thorsten Peter, David Vilar, Daniel Deutsch, Mara Finkelstein, Juraj Juraska, and Markus Freitag. 2023 · 2023
Closest in time.
BLEURT has universal translations: An analysis of automatic metrics by minimum risk training
Yiming Yan, Tao Wang, Chengqi Zhao, Shujian Huang, Jiajun Chen, and Mingxuan Wang. 2023 · 2023
Closest in time.
Navigating the metrics maze: Reconciling score magnitudes and accuracies
Tom Kocmi, Vilém Zouhar, Christian Federmann, and Matt Post. 2024 · 2024
Closest in time.
Quality and quantity of machine translation references for automated metrics
Vilém Zouhar and Ondřej Bojar. 2024 · 2024
Closest in time.