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 proposes a simple and effective novel for generating synthetic bilingual data without using external resources as in previous approaches. Moreover, some works recently have shown that multilingual translation or transfer learning can boost the translation quality in low-resource situations. However, for logographic languages such as Chinese or Japanese, this approach is still limited due to the differences in translation units in the vocabularies. Although Japanese texts contain Kanji characters that are derived from Chinese characters, and they are quite homologous in sharp and meaning, the word orders in the sentences of these languages have a big divergence. Our study will investigate these impacts in machine translation. In addition, a combined pre-trained model is also leveraged to demonstrate the efficacy of translation tasks in the more high-resource scenario. Our experiments present performance improvements up to +6.2 and +7.8 BLEU scores over bilingual baseline systems on two low-resource translation tasks from Chinese to Vietnamese and Japanese to Vietnamese

Translation \u3cem\u3eal Mercato del Pesce\u3c/em\u3e: The Importance of Human Input for Machine Translation

This thesis investigates translation of Italian idioms and metaphors into English, and the difficulties encountered by Machine Translation in this process. I use a framework of foreign concepts to explain many of the difficulties, as well as interviews with native Italian and English speakers to provide further context for the cultural knowledge encoded in figurative language. I conclude that in Machine Translation a consistent human input interface as well as a continuous training in language corpora is crucial to improve the accuracy of translated metaphors and idioms, using Italian to English translation as a case study