Rule Based Transliteration Scheme for English to Punjabi

Machine Transliteration has come out to be an emerging and a very important research area in the field of machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper transliteration of name entities plays a very significant role in improving the quality of machine translation. In this paper we are doing machine transliteration for English-Punjabi language pair using rule based approach. We have constructed some rules for syllabification. Syllabification is the process to extract or separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper names and location). For those words which do not come under the category of name entities, separate probabilities are being calculated by using relative frequency through a statistical machine translation toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to Punjabi

MaTrEx: the DCU machine translation system for ICON 2008

In this paper, we give a description of the machine translation system developed at DCU that was used for our participation in the NLP Tools Contest of the International Conference on Natural Language Processing (ICON 2008). This was our first ever attempt at working on any Indian language. In this participation, we focus on various techniques for word and phrase alignment to improve system quality. For the English-Hindi translation task we exploit source-language reordering. We also carried out experiments combining both in-domain and out-of-domain data to improve the system performance and, as a post-processing step we transliterate out-of-vocabulary items

Experiments on domain adaptation for English-Hindi SMT

Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for English—Hindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline