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Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages

Authors Bharathi Raja Chakravarthi , Mihael Arcan, John P. McCrae



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Bharathi Raja Chakravarthi
  • Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland
Mihael Arcan
  • Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland
John P. McCrae
  • Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland

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Bharathi Raja Chakravarthi, Mihael Arcan, and John P. McCrae. Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 6:1-6:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.LDK.2019.6

Abstract

Under-resourced languages are a significant challenge for statistical approaches to machine translation, and recently it has been shown that the usage of training data from closely-related languages can improve machine translation quality of these languages. While languages within the same language family share many properties, many under-resourced languages are written in their own native script, which makes taking advantage of these language similarities difficult. In this paper, we propose to alleviate the problem of different scripts by transcribing the native script into common representation i.e. the Latin script or the International Phonetic Alphabet (IPA). In particular, we compare the difference between coarse-grained transliteration to the Latin script and fine-grained IPA transliteration. We performed experiments on the language pairs English-Tamil, English-Telugu, and English-Kannada translation task. Our results show improvements in terms of the BLEU, METEOR and chrF scores from transliteration and we find that the transliteration into the Latin script outperforms the fine-grained IPA transcription.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine translation
Keywords
  • Under-resourced languages
  • Machine translation
  • Dravidian languages
  • Phonetic transcription
  • Transliteration
  • International Phonetic Alphabet
  • IPA
  • Multilingual machine translation
  • Multilingual data

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