@inproceedings{park-etal-2022-priming,
title = "Priming {A}ncient {K}orean Neural Machine Translation",
author = "Park, Chanjun and
Lee, Seolhwa and
Seo, Jaehyung and
Moon, Hyeonseok and
Eo, Sugyeong and
Lim, Heuiseok",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.3",
pages = "22--28",
abstract = "In recent years, there has been an increasing need for the restoration and translation of historical languages. In this study, we attempt to translate historical records in ancient Korean language based on neural machine translation (NMT). Inspired by priming, a cognitive science theory that two different stimuli influence each other, we propose novel priming ancient-Korean NMT (AKNMT) using bilingual subword embedding initialization with structural property awareness in the ancient documents. Finally, we obtain state-of-the-art results in the AKNMT task. To the best of our knowledge, we confirm the possibility of developing a human-centric model that incorporates the concepts of cognitive science and analyzes the result from the perspective of interference and cognitive dissonance theory for the first time.",
}
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%0 Conference Proceedings
%T Priming Ancient Korean Neural Machine Translation
%A Park, Chanjun
%A Lee, Seolhwa
%A Seo, Jaehyung
%A Moon, Hyeonseok
%A Eo, Sugyeong
%A Lim, Heuiseok
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F park-etal-2022-priming
%X In recent years, there has been an increasing need for the restoration and translation of historical languages. In this study, we attempt to translate historical records in ancient Korean language based on neural machine translation (NMT). Inspired by priming, a cognitive science theory that two different stimuli influence each other, we propose novel priming ancient-Korean NMT (AKNMT) using bilingual subword embedding initialization with structural property awareness in the ancient documents. Finally, we obtain state-of-the-art results in the AKNMT task. To the best of our knowledge, we confirm the possibility of developing a human-centric model that incorporates the concepts of cognitive science and analyzes the result from the perspective of interference and cognitive dissonance theory for the first time.
%U https://aclanthology.org/2022.lrec-1.3
%P 22-28
Markdown (Informal)
[Priming Ancient Korean Neural Machine Translation](https://aclanthology.org/2022.lrec-1.3) (Park et al., LREC 2022)
ACL
- Chanjun Park, Seolhwa Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, and Heuiseok Lim. 2022. Priming Ancient Korean Neural Machine Translation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 22–28, Marseille, France. European Language Resources Association.