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Statistical-based system combination approach to gain advantages over different machine translation systems.

Debajyoty Banik1,2, Asif Ekbal1,2, Pushpak Bhattacharyya1,2

  • 1Department of Computer Science and Engineering, India.

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|November 6, 2019
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Summary

This study introduces an improved statistical system combination for machine translation, integrating WordNet and word2vec to enhance phrase scoring and accuracy. The novel approach outperforms individual systems and existing combination models.

Keywords:
Hierarchical machine translation (Hiero) systemsMachine translationNeural machine translation (NMT)Neural networkPhrase-based statistical machine translation (PBSMT)Statistical approachSystem combination method

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Machine translation systems possess unique strengths but often lack comprehensive performance.
  • Combining multiple systems offers potential for improved translation quality by leveraging diverse advantages.

Purpose of the Study:

  • To develop an improved statistical system combination approach for machine translation.
  • To enhance the accuracy and quality of machine translation by effectively merging outputs from different systems.

Main Methods:

  • A three-step approach: pair alignment (using WordNet to prevent duplication), hypothesis building (decoding), and scoring (using word2vec).
  • Integration of WordNet for semantic alignment and word2vec for contextual scoring of translated phrases.
  • Combination of Hierarchical machine translation, Bing Microsoft Translate, and Google Translate outputs.

Main Results:

  • The proposed system combination model demonstrates improved machine translation accuracy.
  • Incorporating WordNet and word2vec significantly enhances the scoring and selection of optimal translation phrases.
  • The system achieved better translation quality compared to individual systems and state-of-the-art combination models across eight language pairs.

Conclusions:

  • The enhanced statistical system combination approach effectively leverages the strengths of multiple machine translation systems.
  • WordNet and word2vec integration provides a robust method for accurate phrase alignment and scoring in system combination.
  • This method represents a significant advancement in achieving higher quality machine translation outputs.