Combining CSCBLI, linear transformation, and the iterative VecMap framework yields the top lexicon-induction accuracy on English with Sinhala, Tamil, and Punjabi, but the gains are small and the reported scores come from tuning on the evaluation sets.
Data Augmentation and Terminology Integration for Domain-Specific Sinhala-English-Tamil Statistical Machine Translation
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abstract
Out of vocabulary (OOV) is a problem in the context of Machine Translation (MT) in low-resourced languages. When source and/or target languages are morphologically rich, it becomes even worse. Bilingual list integration is an approach to address the OOV problem. This allows more words to be translated than are in the training data. However, since bilingual lists contain words in the base form, it will not translate inflected forms for morphologically rich languages such as Sinhala and Tamil. This paper focuses on data augmentation techniques where bilingual lexicon terms are expanded based on case-markers with the objective of generating new words, to be used in Statistical machine Translation (SMT). This data augmentation technique for dictionary terms shows improved BLEU scores for Sinhala-English SMT.
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Unsupervised Bilingual Lexicon Induction for Low Resource Languages
Combining CSCBLI, linear transformation, and the iterative VecMap framework yields the top lexicon-induction accuracy on English with Sinhala, Tamil, and Punjabi, but the gains are small and the reported scores come from tuning on the evaluation sets.