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

Identifying Semantic Divergences in Parallel Text without Annotations

Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.Comment: Accepted as a full paper to NAACL 201

Providing Language Services in Small Health Care Provider Settings: Examples From the Field

Assesses recent innovations in language service programs and activities at healthcare provider settings with ten or fewer clinicians. Includes an eight-step plan to help providers develop a strategy to meet the needs of their patients