PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English

Kelechi Ogueji, Orevaoghene Ahia
12/7/2019
cs.CLcs.LG

Official code: keleog/PidginUNMT (81 stars, last commit 2020-11-23).

Abstract

Over 800 languages are spoken across West Africa. Despite the obvious diversity among people who speak these languages, one language significantly unifies them all - West African Pidgin English. There are at least 80 million speakers of West African Pidgin English. However, there is no known natural language processing (NLP) work on this language. In this work, we perform the first NLP work on the most popular variant of the language, providing three major contributions. First, the provision of a Pidgin corpus of over 56000 sentences, which is the largest we know of. Secondly, the training of the first ever cross-lingual embedding between Pidgin and English. This aligned embedding will be helpful in the performance of various downstream tasks between English and Pidgin. Thirdly, the training of an Unsupervised Neural Machine Translation model between Pidgin and English which achieves BLEU scores of 7.93 from Pidgin to English, and 5.18 from English to Pidgin. In all, this work greatly reduces the barrier of entry for future NLP works on West African Pidgin English.

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Code Implementations(1)

keleog/PidginUNMTOfficial100%

Unsupervised Neural Machine Translation from West African Pidgin (Creole) to English without a single parallel sentence

8132Python, Jupyter NotebookAug 12, 20195 years agoNOASSERTION

Cite this paper

@article{ogueji2019pidginunmt,
  title  = {PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English},
  author = {Kelechi Ogueji and Orevaoghene Ahia},
  year   = {2019},
  eprint = {1912.03444},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1912.03444}
}

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