Fetching the paper…
Reading the bibliography…
Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation.
Choosing transfer languages for cross-lingual learning
Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, et al. 2019 · 1905
Earlier work this paper cites.
Revisiting low-resource neural machine translation: A case study
Rico Sennrich and Biao Zhang. 2019 · 1905
Earlier work this paper cites.
Isaac Caswell, Ciprian Chelba, and David Grangier. 2019 · 1906
Earlier work this paper cites.
A focus on neural machine translation for african languages
Laura Martinus and Jade Z Abbott. 2019 · 1906
Earlier work this paper cites.
On the evaluation of machine translation systems trained with back-translation
Sergey Edunov, Myle Ott, Marc’Aurelio Ranzato, and Michael Auli. 2019 · 1908
Earlier work this paper cites.
The distance between two widely separated points on the surface of the earth
Walter D Lambert. 1942 · 1942
Earlier work this paper cites.
Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations
Thaddeus Vincenty. 1975 · 1975
Earlier work this paper cites.
Swahili and Sabaki: A linguistic history , volume 121
Derek Nurse, Thomas J Hinnebusch, and Gérard Philipson. 1993 · 1993
Earlier work this paper cites.
Population subdivision in marine environments: the contributions of biogeography, geographical distance and discontinuous habitat to genetic differentiation in a blennioid fish, axoclinus nigricaudus
C Riginos and MW Nachman. 2001 · 2001
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.
Low resource neural machine translation: A benchmark for five african languages
Surafel M Lakew, Matteo Negri, and Marco Turchi. 2020 · 2003
Cited alongside, same era.
Dynamic data selection and weighting for iterative back-translation
Zi-Yi Dou, Antonios Anastasopoulos, and Graham Neubig. 2020 · 2004
Cited alongside, same era.
On the relation between structural diversity and geographical distance among languages: observations and computer simulations
Eric W Holman, Christian Schulze, Dietrich Stauffer, and Søren Wichmann. 2007 · 2007
Cited alongside, same era.
Rtt measurement and its dependence on the real geographical distance
Ondrej Krajsa and Lucie Fojtova. 2011 · 2011
Cited alongside, same era.
Advances in dialectal arabic speech recognition: A study using twitter to improve egyptian asr
Ahmed Ali, Hamdy Mubarak, and Stephan Vogel. 2014 · 2014
Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
Later among the works it cites.
Marian: Fast neural machine translation in c++
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, et al. 2018 · 2018
Later among the works it cites.
Rapid adaptation of neural machine translation to new languages
Graham Neubig and Junjie Hu. 2018 · 2018
Later among the works it cites.
Decoding strategies for improving low-resource machine translation
Chanjun Park, Yeongwook Yang, Kinam Park, and Heuiseok Lim. 2020 · 2020
Later among the works it cites.
Motivations and willingness to provide care from a geographical distance, and the impact of distance care on caregivers’ mental and physical health: A mixed-method systematic review protocol
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Introduction to the special issue on processing under-resourced languages
Laurent Besacier, Etienne Barnard, Alexey Karpov, and Tanja Schultz. 2014 · 2014
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
Cited alongside, same era.
Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
Cited alongside, same era.
Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
Cited alongside, same era.
Transfer learning across low-resource, related languages for neural machine translation
Toan Q Nguyen and David Chiang. 2017 · 2017
Cited alongside, same era.
Eva Bei, Mikołaj Zarzycki, Val Morrison, and Noa Vilchinsky. 2021 · 2021
Later among the works it cites.
Survey of low-resource machine translation
Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, and Alexandra Birch. 2021 · 2021
Later among the works it cites.
Umsuka english - isizulu parallel corpus
Rooweither Mabuya, Jade Abbott, and Vukosi Marivate. 2021 · 2021
Later among the works it cites.
Canonical and surface morphological segmentation for nguni languages
Tumi Moeng, Sheldon Reay, Aaron Daniels, and Jan Buys. 2021 · 2021
Later among the works it cites.
Low-resource neural machine translation for southern african languages
Evander Nyoni and Bruce A Bassett. 2021 · 2021
Later among the works it cites.
Cost-effective training in low-resource neural machine translation
Sai Koneru, Danni Liu, and Jan Niehues. 2022 · 2022
Closest in time.