基於Transformers深度學習模型建造之高效率漢英新聞雙語檢索系統

徐嘉煜

doi:10.6342/NTU202200561

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基於Transformers深度學習模型建造之高效率漢英新聞雙語檢索系統

Applications of Transformers: Constructing a High-Productivity News Translation-focused Bilingual Concordancer

徐嘉煜 , Masters　　Advisor：高照明　　

英文 DOI： 10.6342/NTU202200561

平行語料庫；雙語檢索系統；雙語語料對齊；句子嵌入； Transformer ； BERT ； sentence transformer ； parallel corpus ； bilingual concordancer ； bitext alignment ； sentence embeddings ； transformer ； BERT ； sentence transformer

Abstract │ Reference (64) │ Altmetrics

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Translation (or parallel) corpora, a special type of text corpora, have been instrumental in the field of translation studies and in translator education (Bernardini, Stewart,   Zanettin, 2003). The advent of statistical and, more recently, neural network-based machine translation (MT) systems has made the importance of parallel corpora even more pronounced, as they serve as the “labeled”, or “supervised”, training data essential for the success of such systems. However, even with the vast improvements MT systems have achieved over the past several years, translators and translation educators still rely heavily on parallel corpora, in the form of bilingual concordancer, in their day-to-day work. In this study, we aim to address three areas concerning the creation of a highly productive Chinese-English concordancer: (a) accuracy in word alignment for parallel sentences; (b) accuracy in sentence alignment for document-aligned parallel corpora; and (c) mining parallel sentences from comparable sources. Our findings suggest that with language models created with the latest artificial neural network-based natural language processing (NLP) technology (specifically, “transformers”), words and phrases in parallel sentences can be accurately aligned, which facilitates the identification of target-language translation equivalents given a source language search word or phrase. Moreover, with sentence transformers, a document- or paragraph-aligned parallel corpus can be transformed into aligned sentences with much reduced effort in manual post-alignment corrections. Finally, we demonstrate that parallel sentences can be harvested profitably from news organizations that offer multilingual news articles, even if pairs of news articles and their translations are not explicitly linked or otherwise indicated on the website.

Reference ( 64 ) 〈TOP〉

Artetxe, M., Schwenk, H. (2019a). Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings. ArXiv:1811.01136 [Cs]. http://arxiv.org/abs/1811.01136
Artetxe, M., Schwenk, H. (2019b). Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond. Transactions of the Association for Computational Linguistics, 7, 597–610. https://doi.org/10.1162/tacl_a_00288
Baker, M. 1993. Corpus linguistics and translation studies: Implications and applications. In Text and technology: In honour of John Sinclair, ed. M. Baker, G. Francis, and E. Tognini-Bonelli, 233–250. Philadelphia: John Benjamins.
Baker, M., Saldanha, G. (eds.) (2020) Routledge Encyclopedia of Translation Studies. 3rd ed. Routledge CRC Press.
Baroni, M., Bernardini, S. (2004). BootCaT: Bootstrapping Corpora and Terms from the Web. In Proceedings of LREC (2004)

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