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Data Augmentation and Terminology Integration for Domain-Specific Sinhala-English-Tamil Statistical Machine Translation

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arxiv 2011.02821 v3 pith:4CNH3TVU submitted 2020-11-05 cs.CL

Aloka Fernando , Surangika Ranathunga , Gihan Dias This is my paper · ORCID
classification cs.CL
keywords dataaugmentationbilinguallanguagesmachinetranslationwordsintegration
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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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Cited by 3 Pith papers

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  1. A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala

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    A new manually annotated English-Tamil-Sinhala parallel NER corpus of 3,835 sentences per language, with benchmarks showing XLM-R outperforms monolingual and Indic models, and a case study where NER output improves En...

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    A new Sinhala text simplification dataset with 3,000 human-written simplifications is released, and intermediate-task transfer learning on mT5/mBART beats prior zero-resource baselines.

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    cs.CL 2024-12 conditional novelty 4.0 of 10

    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 f...

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