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A relationship: word alignment, phrase table, and translation quality.

Liang Tian1, Derek F Wong1, Lidia S Chao1

  • 1Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau.

Thescientificworldjournal
|June 3, 2014
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Summary

Researchers developed a formula to predict machine translation phrase table size based on word alignments. A new pruning method significantly reduces phrase tables by 98% with minimal impact on translation quality.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Machine Translation

Background:

  • Existing machine translation (MT) quality evaluation methods often lack theoretical grounding.
  • The relationship between word alignments, phrase tables, and MT performance is not well-defined by a formal model.

Purpose of the Study:

  • To formulate a mathematical relationship between word alignments and phrase table size.
  • To propose a corpus-motivated technique for pruning large phrase tables.
  • To provide a theoretical basis for understanding MT performance in relation to phrase table characteristics.

Main Methods:

  • Developing a formula to estimate the number of phrase pairs based on word alignment points.
  • Implementing a corpus-motivated pruning strategy to reduce phrase table redundancy.
  • Conducting experiments to validate the formula and evaluate the pruning technique's effectiveness.

Main Results:

  • The proposed formula accurately estimates phrase table size, offering insights into alignment-performance links.
  • The corpus-motivated pruning technique successfully reduces phrase tables by approximately 98%.
  • This reduction is achieved without significant degradation of machine translation quality.

Conclusions:

  • The established formula provides a theoretical framework for analyzing phrase table properties in MT.
  • The effective pruning method significantly enhances efficiency without compromising translation accuracy.
  • This research offers a valuable reference for future studies on MT performance and phrase table optimization.