An Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation: An empirical study of Chinese, Japanese to Vietnamese Neural Machine Translation
Data sparsity is one of the challenges for low-resource language pairs in Neural Machine Translation (NMT). Previous works have presented different approaches for data augmentation, but they mostly require additional resources and obtain low-quality dummy data in the low-resource issue. This paper p...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2022-12-01
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Series: | Applied Artificial Intelligence |
Online Access: | http://dx.doi.org/10.1080/08839514.2022.2101755 |