@inproceedings{park-etal-2019-knu,
title = "{KNU}-{HYUNDAI}{'}s {NMT} system for Scientific Paper and Patent Tasks on{WAT} 2019",
author = "Park, Cheoneum and
Jung, Young-Jun and
Kim, Kihoon and
Kim, Geonyeong and
Jeon, Jae-Won and
Lee, Seongmin and
Kim, Junseok and
Lee, Changki",
editor = "Nakazawa, Toshiaki and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Doi, Nobushige and
Oda, Yusuke and
Bojar, Ond{\v{r}}ej and
Parida, Shantipriya and
Goto, Isao and
Mino, Hidaya",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5208",
doi = "10.18653/v1/D19-5208",
pages = "81--89",
abstract = "In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). We participated in all tasks of ASPEC and JPC2, which included those of Chinese-Japanese, English-Japanese, and Korean-{\textgreater}Japanese. We submitted our transformer-based NMT system with built using the following methods: a) relative positioning method for pairwise relationships between the input elements, b) back-translation and multi-source translation for data augmentation, c) right-to-left (r2l)-reranking model robust against error propagation in autoregressive architectures such as decoders, and d) checkpoint ensemble models, which selected the top three models with the best validation bilingual evaluation understudy (BLEU) . We have reported the translation results on the two aforementioned tasks. We performed well in both the tasks and were ranked first in terms of the BLEU scores in all the JPC2 subtasks we participated in.",
}
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<abstract>In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). We participated in all tasks of ASPEC and JPC2, which included those of Chinese-Japanese, English-Japanese, and Korean-\textgreaterJapanese. We submitted our transformer-based NMT system with built using the following methods: a) relative positioning method for pairwise relationships between the input elements, b) back-translation and multi-source translation for data augmentation, c) right-to-left (r2l)-reranking model robust against error propagation in autoregressive architectures such as decoders, and d) checkpoint ensemble models, which selected the top three models with the best validation bilingual evaluation understudy (BLEU) . We have reported the translation results on the two aforementioned tasks. We performed well in both the tasks and were ranked first in terms of the BLEU scores in all the JPC2 subtasks we participated in.</abstract>
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%0 Conference Proceedings
%T KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019
%A Park, Cheoneum
%A Jung, Young-Jun
%A Kim, Kihoon
%A Kim, Geonyeong
%A Jeon, Jae-Won
%A Lee, Seongmin
%A Kim, Junseok
%A Lee, Changki
%Y Nakazawa, Toshiaki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Doi, Nobushige
%Y Oda, Yusuke
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F park-etal-2019-knu
%X In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). We participated in all tasks of ASPEC and JPC2, which included those of Chinese-Japanese, English-Japanese, and Korean-\textgreaterJapanese. We submitted our transformer-based NMT system with built using the following methods: a) relative positioning method for pairwise relationships between the input elements, b) back-translation and multi-source translation for data augmentation, c) right-to-left (r2l)-reranking model robust against error propagation in autoregressive architectures such as decoders, and d) checkpoint ensemble models, which selected the top three models with the best validation bilingual evaluation understudy (BLEU) . We have reported the translation results on the two aforementioned tasks. We performed well in both the tasks and were ranked first in terms of the BLEU scores in all the JPC2 subtasks we participated in.
%R 10.18653/v1/D19-5208
%U https://aclanthology.org/D19-5208
%U https://doi.org/10.18653/v1/D19-5208
%P 81-89
Markdown (Informal)
[KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019](https://aclanthology.org/D19-5208) (Park et al., WAT 2019)
ACL
- Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, and Changki Lee. 2019. KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019. In Proceedings of the 6th Workshop on Asian Translation, pages 81–89, Hong Kong, China. Association for Computational Linguistics.