Priming Ancient Korean Neural Machine Translation

Chanjun Park, Seolhwa Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, Heuiseok Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2022 Language Resources and Evaluation Conference, LREC 2022
EditorsNicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, Stelios Piperidis
PublisherEuropean Language Resources Association (ELRA)
Pages22-28
Number of pages7
ISBN (Electronic)9791095546726
Publication statusPublished - 2022
Event13th International Conference on Language Resources and Evaluation Conference, LREC 2022 - Marseille, France
Duration: 2022 Jun 202022 Jun 25

Publication series

Name2022 Language Resources and Evaluation Conference, LREC 2022

Conference

Conference13th International Conference on Language Resources and Evaluation Conference, LREC 2022
Country/TerritoryFrance
CityMarseille
Period22/6/2022/6/25

Bibliographical note

Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-0-01405) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation) and supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).

Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

Keywords

  • Ancient-Korean Neural Machine Translation
  • Neural Machine Translation
  • Priming

ASJC Scopus subject areas

  • Language and Linguistics
  • Library and Information Sciences
  • Linguistics and Language
  • Education

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