9 research outputs found
Classifier-Based Text Simplification for Improved Machine Translation
Machine Translation is one of the research fields of Computational
Linguistics. The objective of many MT Researchers is to develop an MT System
that produce good quality and high accuracy output translations and which also
covers maximum language pairs. As internet and Globalization is increasing day
by day, we need a way that improves the quality of translation. For this
reason, we have developed a Classifier based Text Simplification Model for
English-Hindi Machine Translation Systems. We have used support vector machines
and Na\"ive Bayes Classifier to develop this model. We have also evaluated the
performance of these classifiers.Comment: In Proceedings of International Conference on Advances in Computer
Engineering and Applications 201
Latest trends in hybrid machine translation and its applications
This survey on hybrid machine translation (MT) is motivated by the fact that hybridization techniques have become popular as they attempt to combine the best characteristics of highly advanced pure rule or corpus-based MT approaches. Existing research typically covers either simple or more complex architectures guided by either rule or corpus-based approaches. The goal is to combine the best properties of each type.
This survey provides a detailed overview of the modification of the standard rule-based architecture to include statistical knowl- edge, the introduction of rules in corpus-based approaches, and the hybridization of approaches within this last single category. The principal aim here is to cover the leading research and progress in this field of MT and in several related applications.Peer ReviewedPostprint (published version
Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System
Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model. The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language. This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used.  With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system