short-paper

Exploiting Japanese–Chinese Cognates with Shared Private Representations for NMT

Authors:

Piao ShiAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 1

Article No.: 28, Pages 1 - 12

https://doi.org/10.1145/3533429

Published: 25 November 2022 Publication History

Abstract

Neural machine translation has achieved remarkable progress over the past several years; however, little attention has been paid to machine translation (MT) between Japanese and Chinese, which share a large proportion of cognate words that can be utilized as additional linguistic knowledge to enhance translation performance. In this article, we seek to strengthen the semantic correlation between Japanese and Chinese by leveraging cognate words that share common Chinese characters. Specifically, we experiment with three strategies: (1) a shared vocabulary with cognate lexicon induction, which models the commonality between source and target cognates; (2) a shared private representation with a dynamic gating mechanism, which models the language-specific features on the source side; and (3) an embedding shortcut, which enables the decoder to access the shared private representation with shortest distance and aids the training process. The experiments and analysis presented in this article demonstrate that our proposed approaches can significantly improve the performance of both Japanese-to-Chinese and Chinese-to-Japanese translations and verify the effectiveness of exploiting Japanese–Chinese cognates for MT.

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Cited By

Li ZSun XRen FMa JHuang DShi P(2023)Multilingual BERT-based Word Alignment By Incorporating Common Chinese CharactersACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359463422:6(1-13)Online publication date: 19-Jun-2023
https://doi.org/10.1145/3594634

Index Terms

Exploiting Japanese–Chinese Cognates with Shared Private Representations for NMT
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation
      2. Natural language generation

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 1

January 2023

340 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3572718

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 November 2022

Online AM: 05 May 2022

Accepted: 24 April 2022

Revised: 06 April 2022

Received: 31 October 2021

Published in TALLIP Volume 22, Issue 1

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Cited By

Li ZSun XRen FMa JHuang DShi P(2023)Multilingual BERT-based Word Alignment By Incorporating Common Chinese CharactersACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359463422:6(1-13)Online publication date: 19-Jun-2023
https://doi.org/10.1145/3594634

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