Phrase Pair Mappings for Hindi-English Statistical Machine Translation
This work addresses translation quality for resource-poor languages like Hindi, but it is incremental as it builds on existing methods with new lexical data.
The paper tackled improving Hindi-English statistical machine translation by augmenting the parallel corpus with lexical resources like synset words and verb phrases, resulting in incremental growth in translation quality as shown through evaluations and error analysis.
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.