Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT

TL;DR

This paper enhances pivot-based statistical machine translation by integrating lexical models into phrase tables and introducing a new pruning method, leading to improved translation coverage and accuracy, especially in Chinese-Japanese translation.

Contribution

It proposes a novel approach to augment pivot phrase tables with lexical models and a phrase table pruning method considering both source and target coverage.

Findings

Pruning method outperforms conventional approaches.

Inclusion of lexical entries increases phrase coverage.

Achieved better translation results in Chinese-Japanese translation.

Abstract

Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist. The combination of source-to-pivot and pivot-to-target translation models can induce a new translation model through the pivot language. However, the errors in two models may compound as noise, and still, the combined model may suffer from a serious phrase sparsity problem. In this paper, we directly employ the word lexical model in IBM models as an additional resource to augment pivot phrase table. In addition, we also propose a phrase table pruning method which takes into account both of the source and target phrasal coverage. Experimental result shows that our pruning method significantly outperforms the conventional one, which only considers source side phrasal coverage. Furthermore, by including the entries in the lexicon model, the phrase coverage increased, and we achieved improved results in Chinese-to-Japanese translation using English as pivot language.