WCC-EC 2.0: Enhancing Neural Machine Translation with a 1.6M+ Web-Crawled English-Chinese Parallel Corpus

This research introduces WCC-EC 2.0 (Web-Crawled Corpus—English and Chinese), a comprehensive parallel corpus designed for enhancing Neural Machine Translation (NMT), featuring over 1.6 million English-Chinese sentence pairs meticulously gathered via web crawling. This corpus, extracted through an a...

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Bibliographic Details
Published in:Electronics (Basel) 2024-04, Vol.13 (7), p.1381
Main Authors: Zhang, Jinyi, Su, Ke, Tian, Ye, Matsumoto, Tadahiro
Format: Article
Language:English
Subjects:
Bilingualism
Computational linguistics
Datasets
English language
Language
Language processing
Lyrics
Machine translation
Model accuracy
Multilingualism
Music
Natural language interfaces
R&D
Research & development
Resilience
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Summary:This research introduces WCC-EC 2.0 (Web-Crawled Corpus—English and Chinese), a comprehensive parallel corpus designed for enhancing Neural Machine Translation (NMT), featuring over 1.6 million English-Chinese sentence pairs meticulously gathered via web crawling. This corpus, extracted through an advanced web crawler, showcases the vast linguistic diversity and richness of English and Chinese, uniquely spanning the rarely covered news and music domains. Our methodical approach in web crawling and corpus assembly, coupled with rigorous experiments and manual evaluations, demonstrated its superiority by achieving high BLEU scores, marking significant strides in translation accuracy and model resilience. Its inclusion of these specific areas adds significant value, providing a unique dataset that enriches the scope for NMT research and development. With the rise of NMT technology, WCC-EC 2.0 emerges not only as an invaluable resource for researchers and developers, but also as a pivotal tool for improving translation accuracy, training more resilient models, and promoting interlingual communication.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13071381