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.

References

[1]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR’15).

[2]

Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George F. Foster, Llion Jones, Niki Parmar, Mike Schuster, Zhifeng Chen, Yonghui Wu, and Macduff Hughes. 2018. The best of both worlds: Combining recent advances in neural machine translation. CoRR abs/1804.09849 (2018).

[3]

Chenhui Chu, Toshiaki Nakazawa, Daisuke Kawahara, and Sadao Kurohashi. 2013. Chinese-japanese machine translation exploiting chinese characters. ACM Trans. As. Lang. Inf. Process. 12, 4 (2013), 16:1–16:25.

[4]

Chenhui Chu, Toshiaki Nakazawa, and Sadao Kurohashi. 2012. Chinese characters mapping table of japanese, traditional chinese and simplified chinese. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’12). 2149–2152.

[5]

Raj Dabre, Chenhui Chu, and Anoop Kunchukuttan. 2020. A comprehensive survey of multilingual neural machine translation. CoRR abs/2001.01115 (2020).

[6]

Sakshi Dhall, Ashutosh Dhar Dwivedi, Saibal K. Pal, and Gautam Srivastava. 2022. Blockchain-based framework for reducing fake or vicious news spread on social media/messaging platforms. ACM Trans. As. Low Resour. Lang. Inf. Process. 21, 1 (2022), 8:1–8:13.

[7]

Denis Emelin, Ivan Titov, and Rico Sennrich. 2019. Widening the representation bottleneck in neural machine translation with lexical shortcuts. In Proceedings of the Conference on Machine Translation. 102–115.

[8]

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional sequence to sequence learning. In Proceedings of the International Conference on Machine Learning (ICML’17). 1243–1252.

[9]

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017. OpenNMT: Open-source toolkit for neural machine translation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’17). 67–72.

[10]

Shaohui Kuang, Junhui Li, António Branco, Weihua Luo, and Deyi Xiong. 2018. Attention focusing for neural machine translation by bridging source and target embeddings. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’18). Melbourne, Australia, 1767–1776.

[11]

Michiki Kurosawa, Yukio Matsumura, Hayahide Yamagishi, and Mamoru Komachi. 2018. Japanese predicate conjugation for neural machine translation. CoRR abs/1805.10047 (2018).

[12]

Chen-Yu Lee, Saining Xie, Patrick W. Gallagher, Zhengyou Zhang, and Zhuowen Tu. 2014. Deeply-supervised nets. CoRR abs/1409.5185 (2014).

[13]

Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao, and Jingbo Zhu. 2019. Shared-private bilingual word embeddings for neural machine translation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’19). 3613–3622.

[14]

Toshiaki Nakazawa, Manabu Yaguchi, Kiyotaka Uchimoto, Masao Utiyama, Eiichiro Sumita, Sadao Kurohashi, and Hitoshi Isahara. 2016. ASPEC: Asian scientific paper excerpt corpus. In Proceedings of International Conference on Language Resources and Evaluation (LREC’16).

[15]

Ashokkumar Palanivinayagam, G. Siva Shankar, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, and Thippa Reddy Gadekallu. 2021. A two-stage text feature selection algorithm for improving text classification. ACM Trans. As. Low Resour. Lang. Inf. Process. 20, 3 (2021), 49:1–49:19.

[16]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’02). 311–318.

[17]

Jeonghyeok Park and Hai Zhao. 2019. Korean-to-chinese machine translation using chinese character as pivot clue. CoRR abs/1911.11008 (2019).

[18]

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’16). 1715–1725.

[19]

Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015. Highway networks. CoRR abs/1505.00387 (2015).

[20]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the Conference and Workshop on Neural Information Processing Systems (NIPS’14). 3104–3112.

[21]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Conference and Workshop on Neural Information Processing Systems (NIPS’17). 5998–6008.

[22]

Changhan Wang, Kyunghyun Cho, and Jiatao Gu. 2020. Neural machine translation with byte-level subwords. In Proceedings of the Annual AAAI Conference on Artificial Intelligence (AAAI’20). New York, NY, 9154–9160.

[23]

Jilei Wang, Shiying Luo, Weiyan Shi, Tao Dai, and Shu-Tao Xia. 2018. Exploiting common characters in chinese and japanese to learn cross-lingual word embeddings via matrix factorization. In Proceedings of the 3rd Workshop on Representation Learning for NLP @ACL. 113–121.

[24]

Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama, and Eiichiro Sumita. 2018. Graph-based bilingual word embedding for statistical machine translation. ACM Trans. As. Low Resour. Lang. Inf. Process. 17, 4 (October2018), 31:1–31:23.

[25]

Lijun Wu, Fei Tian, Li Zhao, Jianhuang Lai, and TieYan Liu. 2018. Word attention for sequence to sequence text understanding. In Proceedings of the 32th Conference on Artificial Intelligence. 5578–5585.

[26]

Longtu Zhang and Mamoru Komachi. 2021. Using sub-character level information for neural machine translation of logographic languages. ACM Trans. As. Low Resour. Lang. Inf. Process. 20, 2 (2021), 31:1–31:15.

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

Recommendations

Multilingual BERT-based Word Alignment By Incorporating Common Chinese Characters
Word alignment is an important task of detecting translation equivalents between a sentence pair. Although word alignment is no longer necessarily needed for neural machine translation, it’s still useful in a wealth of applications, e.g., bilingual ...
Chinese-Japanese Machine Translation Exploiting Chinese Characters

The Chinese and Japanese languages share Chinese characters. Since the Chinese characters in Japanese originated from ancient China, many common Chinese characters exist between these two languages. Since Chinese characters contain significant semantic ...
Readability Factors of Japanese Text Classification
Databases in Networked Information Systems
Abstract
Languages with comprehensive alphabets in written form, such as the ideographic system of Chinese adopted to Japanese, have specific combinatorial potential for text summarization and categorization. Modern Japanese text is composed of strings ...

Comments

Information & Contributors

Information

Published In

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].

Publisher

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Refereed

Funding Sources

National Key Research and Development Program of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
244
Total Downloads

Downloads (Last 12 months)51
Downloads (Last 6 weeks)3

Reflects downloads up to 04 Sep 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents