Phrase Pair Mappings for Hindi-English Statistical Machine Translation

TL;DR

This paper develops phrase pair mappings and lexical resources to improve Hindi-English statistical machine translation, demonstrating incremental quality improvements through various lexical augmentations.

Contribution

It introduces new phrase pair mappings and lexical resource integration methods to enhance SMT performance for resource-poor languages.

Findings

Lexical resource augmentation improves translation quality.

Incremental growth in translation accuracy with more lexical resources.

Both automatic and subjective evaluations confirm improvements.

Abstract

In this paper, we present our work on the creation of lexical resources for the Machine Translation between English and Hindi. We describes the development of phrase pair mappings for our experiments and the comparative performance evaluation between different trained models on top of the baseline Statistical Machine Translation system. We focused on augmenting the parallel corpus with more vocabulary as well as with various inflected forms by exploring different ways. We have augmented the training corpus with various lexical resources such as lexical words, synset words, function words and verb phrases. We have described the case studies, automatic and subjective evaluations, detailed error analysis for both the English to Hindi and Hindi to English machine translation systems. We further analyzed that, there is an incremental growth in the quality of machine translation with the usage of various lexical resources. Thus lexical resources do help uplift the translation quality of resource poor langugaes.