A Systematic Comparison between Various Statistical Alignment Models for Statistical English-Vietnamese Phrase-Based Translation

Abstract

In statistical phrase-based machine translation, the step of phrase learning heavily relies on word alignments. This paper provides a systematic comparison of applying various statistical alignment models for statistical English-Vietnamese phrase-based machine translation. We will also invest a heuristic method for elevating the translation quality of using higher word-alignment models by improving the quality of lexical modelling. In detail, we will experimentally show that taking up the lexical translation seems to be an appropriate approach to force "higher" word-based translation models be able to efficiently "boost" their merits. We hope this work will be a reliable comparison benchmark for other studies on using and improving the statistical alignment models for English-Vietnamese machine translation systems.