research-article

Linguistically Driven Multi-Task Pre-Training for Low-Resource Neural Machine Translation

Authors:

Sadao KurohashiAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 4

Article No.: 68, Pages 1 - 29

https://doi.org/10.1145/3491065

Published: 19 January 2022 Publication History

Abstract

In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English. JASS focuses on masking and reordering Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on phrase structure masking and reordering tasks. Experiments on ASPEC Japanese–English & Japanese–Chinese, Wikipedia Japanese–Chinese, News English–Korean corpora demonstrate that JASS and ENSS outperform MASS and other existing language-agnostic pre-training methods by up to +2.9 BLEU points for the Japanese–English tasks, up to +7.0 BLEU points for the Japanese–Chinese tasks and up to +1.3 BLEU points for English–Korean tasks. Empirical analysis, which focuses on the relationship between individual parts in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and ENSS. Adequacy evaluation using LASER, human evaluation, and case studies reveals that our proposed methods significantly outperform pre-training methods without injected linguistic knowledge and they have a larger positive impact on the adequacy as compared to the fluency.

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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 21, Issue 4

July 2022

464 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3511099

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 19 January 2022

Accepted: 01 November 2021

Revised: 01 August 2021

Received: 01 March 2021

Published in TALLIP Volume 21, Issue 4

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Hassan MYu ZWang JLi YGao SYang SMao C(2024)LKMT: Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and EnglishComputers, Materials & Continua10.32604/cmc.2024.05467381:1(951-969)Online publication date: 2024
https://doi.org/10.32604/cmc.2024.054673
Mao ZChu CKurohashi S(2024)EMS: Efficient and Effective Massively Multilingual Sentence Embedding LearningIEEE/ACM Transactions on Audio, Speech, and Language Processing10.1109/TASLP.2024.340206432(2841-2856)Online publication date: 2024
https://doi.org/10.1109/TASLP.2024.3402064
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Jiang WLi MShabaz MSharma AHaq M(2022)Generation of Voice Signal Tone Sandhi and Melody Based on Convolutional Neural NetworkACM Transactions on Asian and Low-Resource Language Information Processing10.1145/354556922:5(1-13)Online publication date: 19-Sep-2022
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