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Is the Corpus Ready for Machine Translation? A Case Study with Python to Pseudo-Code Corpus.

Sawan Rai1, Ramesh Chandra Belwal1, Atul Gupta1

  • 1PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, 482005 India.

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Summary
This summary is machine-generated.

Data quality significantly impacts machine translation performance. Cleaning a Python-to-pseudo-code corpus improved statistical and neural machine translation models by over 10% on BLEU score.

Keywords:
Neural machine translationParallel corpusPseudo-codePython codeStatistical machine translation

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Area of Science:

  • Natural Language Processing
  • Software Engineering
  • Machine Learning

Background:

  • Data availability is crucial for state-of-the-art machine translation (MT).
  • Researchers often evaluate new MT techniques using publicly available parallel corpora.
  • Corpus correctness and consistency are vital for reliable learning algorithm performance.

Purpose of the Study:

  • Investigate the relevance of a public Python-to-pseudo-code parallel corpus for automated documentation.
  • Assess the impact of corpus quality on machine translation model performance.

Main Methods:

  • Utilized statistical machine translation (SMT) and neural machine translation (NMT) models.
  • Identified and addressed issues in the parallel corpus, including overlapping instances, inconsistent styles, incompleteness, and misspellings.
  • Compared model performance before and after corpus cleaning.

Main Results:

  • The Python-to-pseudo-code corpus contained significant quality issues.
  • Corpus discrepancies substantially influenced machine translation model performance.
  • Cleaning the corpus led to a significant performance improvement (over 10% BLEU score) for both SMT and NMT models.

Conclusions:

  • Publicly available corpora may contain errors that lead to incorrect research conclusions.
  • Data cleaning is a critical step for reliable machine translation research.
  • Improved corpus quality directly enhances the performance of automated documentation systems.