Data Augmentation with Diversified Rephrasing for Low-Resource Neural Machine Translation
Loading...
Date
2023-01-01
DOI
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Asia-Pacific Association for Machine Translation
Rights
(c) 2023 The Author/s
CC BY 4.0
CC BY 4.0
Abstract
Data augmentation is an effective way to enhance the performance of neural machine translation models, especially for low-resource languages. Existing data augmentation methods are either at a token level or a sentence level. The data augmented using token level methods lack syntactic diversity and may alter original meanings. Sentence level methods usually generate low-quality source sentences that are not semantically paired with the original target sentences. In this paper, we propose a novel data augmentation method to generate diverse, high-quality and meaning-preserved new instances. Our method leverages high-quality translation models trained with high-resource languages to rephrase an original sentence by translating it into an intermediate language and then back to the original language. Through this process, the high-performing translation models guarantee the quality of the rephrased sentences, and the syntactic knowledge from the intermediate language can bring syntactic diversity to the rephrased sentences. Experimental results show our method can enhance the performance in various low-resource machine translation tasks. Moreover, by combining our method with other techniques that facilitate NMT, we can yield even better results.
Description
Keywords
Citation
Gao Y, Hou F, Jahnke H, Wang R. (2023). Data Augmentation with Diversified Rephrasing for Low-Resource Neural Machine Translation. Utiyama M, Wang R. MT Summit 2023 - Proceedings of 19th Machine Translation Summit. (pp. 35-47). Asia-Pacific Association for Machine Translation.