@inproceedings{nagoudi-etal-2020-growing,
title = "Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation",
author = "Nagoudi, El Moatez Billah and
Abdul-Mageed, Muhammad and
Cavusoglu, Hasan",
editor = "Birch, Alexandra and
Finch, Andrew and
Hayashi, Hiroaki and
Heafield, Kenneth and
Junczys-Dowmunt, Marcin and
Konstas, Ioannis and
Li, Xian and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ngt-1.20",
doi = "10.18653/v1/2020.ngt-1.20",
pages = "169--177",
abstract = "We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). We view MT models at various training stages (i.e., checkpoints) as human learners at different levels. Hence, we employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. From each checkpoint, for our best model, we sample n-Best sequences (n=10) with a beam width =100. We achieve an 37.57 macro F1 with a 6 checkpoint model ensemble on the official shared task test data, outperforming a baseline Amazon translation system of 21.30 macro F1 and ultimately demonstrating the utility of our intuitive method.",
}
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%0 Conference Proceedings
%T Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation
%A Nagoudi, El Moatez Billah
%A Abdul-Mageed, Muhammad
%A Cavusoglu, Hasan
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Hayashi, Hiroaki
%Y Heafield, Kenneth
%Y Junczys-Dowmunt, Marcin
%Y Konstas, Ioannis
%Y Li, Xian
%Y Neubig, Graham
%Y Oda, Yusuke
%S Proceedings of the Fourth Workshop on Neural Generation and Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F nagoudi-etal-2020-growing
%X We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). We view MT models at various training stages (i.e., checkpoints) as human learners at different levels. Hence, we employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. From each checkpoint, for our best model, we sample n-Best sequences (n=10) with a beam width =100. We achieve an 37.57 macro F1 with a 6 checkpoint model ensemble on the official shared task test data, outperforming a baseline Amazon translation system of 21.30 macro F1 and ultimately demonstrating the utility of our intuitive method.
%R 10.18653/v1/2020.ngt-1.20
%U https://aclanthology.org/2020.ngt-1.20
%U https://doi.org/10.18653/v1/2020.ngt-1.20
%P 169-177
Markdown (Informal)
[Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation](https://aclanthology.org/2020.ngt-1.20) (Nagoudi et al., NGT 2020)
ACL