文章基本信息

标题：Investigating backtranslation for the improvement of English-Irish machine translation
本地全文：下载
作者：Meghan Dowling ; Teresa Lynn ; Andy Way 等
期刊名称：Teanga: The Journal of the Irish Association for Applied Linguistics
印刷版ISSN：0332-205X
电子版ISSN：2565-6325
出版年度：2019
卷号：26
页码：1-25
DOI：10.35903/teanga.v26i0.88
出版社：The Irish Association for Applied Linguistics
摘要：In this paper, we discuss the difficulties of building reliable machine translation (MT) systems for the English-Irish (EN-GA) language pair. In the context of limited datasets, we report on assessing the use of backtranslation as a method for creating artificial EN-GA data to increase training data for use in state-ofthe-art data-driven translation systems. We compare our results to our earlier work on EN-GA machine translation (Dowling et al. 2016; 2017; 2018) showing that while our own systems underperform with respect to traditionally reported automatic evaluation metrics, we provide a linguistic analysis to suggest that future work with domain-specific data may prove more successful.
其他摘要：In this paper, we discuss the difficulties of building reliable machine translation systems for the English-Irish (EN-GA) language pair. In the context of limited datasets, we report on assessing the use of backtranslation as a method for creating artificial EN-GA data to increase training data for use state-of-the-art data-driven translation systems. We compare our results to earlier work on EN-GA machine translation by Dowling et al (2016, 2017, 2018) showing that while our own systems do not compare in quality with respect to traditionally reported BLEU metrics, we provide a linguistic analysis to suggest that future work with domain specific data may prove more successful.
关键词：Machine translation; Gaeilge; English; automatic evaluation metrics
其他关键词：Automatic Evaluation Metrics