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Pseudotext Injection and Advance Filtering of Low-Resource Corpus for Neural Machine Translation.

Michael Adjeisah1, Guohua Liu1, Douglas Omwenga Nyabuga1

  • 1School of Computer Science and Technology, Donghua University, Shanghai, China.

Computational Intelligence and Neuroscience
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Summary

This study enhances machine translation for low-resource languages like English-Twi by generating and filtering synthetic data. The approach significantly improves translation quality using novel corpus expansion and filtering techniques.

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Translation (MT)
  • Computational Linguistics

Background:

  • Scaling NLP and MT to low-resource languages presents significant challenges.
  • Defining high-quality parallel corpora in low-resource settings is difficult, especially when only target language data is available.

Purpose of the Study:

  • To improve machine translation performance for low-resource language pairs, specifically English-Twi.
  • To develop and evaluate methods for expanding training data using synthetic-parallel corpora.

Main Methods:

  • Generated synthetic-parallel corpora by translating monolingual target-language data using bootstrapping.
  • Applied unsupervised measurements with squared Mahalanobis distances for sentence parallelism detection.
  • Utilized three sentence-level similarity metrics post-round-trip translation for extensive filtering.

Main Results:

  • Injecting synthetic-parallel corpora and applying advanced filtering significantly boosted MT performance.
  • Demonstrated substantial improvements in BLEU and TER scores compared to baseline systems.
  • Validated the effectiveness of the proposed data augmentation and filtering strategy on diverse parallel corpora.

Conclusions:

  • The proposed method effectively enhances MT systems for low-resource languages.
  • Synthetic data generation combined with robust filtering is a viable strategy for low-resource NLP.
  • This research offers a significant advancement in addressing the challenges of low-resource machine translation.