A Tamil – Telugu Machine Translation System

The development of Machine Translation (MT) is one of the most challenging tasks of Natural Language Processing Applications. In MT there are a number of approaches that are being practiced all over the world, chiefly, they are Direct translations, Interlingual translations, Transfer based translations and a combination of these beside the statistical and corpus based methods. It is a known fact that Indian languages exhibit a considerable amount of diversity between them at every level viz. morphological, syntactic, semantic and lexical levels. In the Transfer Based approach a representation of source language (SL) at certain level is transferred to the corresponding target language (TL) representation. Keeping these in mind, building a Machine Translation System for these languages using Transfer based Method can be non-trivial and challenging. The present paper discusses the successful implementation of the Transfer based Approach to the Machine Translation (MT) System for Hindi<->Telugu. Different resources for this system come from eleven different institutions across India.

2015

The term Machine Translation can be defined as Translation of sentences or words from one language to another language automatically with or without any human involvement. Today Machine Translation Systems plays an important role for sharing the information from one language to another language like Sanskrit to Hindi, Devanagari to English etc., which are life transforming stories available in India. In this work, translation of Kannada to Telugu languages has been considered which is mainly used in southern part of India (Karnataka, Andhra Pradesh, and Telangana). The basic activity of any machine translation application is to manage the vocabulary of words .The existing literature has many machine translation systems like Directed Machine Translation, Interlingual Machine Translation system, Statistical Machine Translation, Hybrid Machine translated System, Transfer Based approach and Corpus Based Machine Translated System etc. In this work, Transfer Based Machine Translation has ...

AIP Conference Proceedings, 2010

Translation is one of the needs of global society for communicating thoughts and ideas of one country with other country. Translation is the process of interpretation of text meaning and subsequent production of equivalent text, also called as communicating same meaning (message) in another language. In this paper we gave detail information on how to convert source language text in to target language text using Transfer Based Approach for machine translation. Here we implemented English to Sanskrit machine translator using transfer based approach. English is global language used for business and communication but large amount of population in India is not using and understand the English. Sanskrit is ancient language of India most of the languages in India are derived from Sanskrit. Sanskrit can be act as an intermediate language for multilingual translation.

This paper describes and evaluates the machine translation systems built for Indian languages-to-Indian languages (IL-ILMT) with special reference to Tamil. It is a consortium project funded by The IL-ILMT systems are built based on the combination of rule-based and statistical approaches. The systems are developed specially for tourism and health domains. The systems used rule based approach as it provides better performance and accuracy if the set of rules is under control. As for as Tamil oriented IL-ILMT consortium systems are concerned, the translation output is not even satisfactory. Most of the ILILMT systems developed under this consortium project are still in the infant stage. We have to work hard to achieve satisfactory results.

Language in India www.languageinindia.com ISSN 1930-2940 Vol. 19:5 , 2019

This research material entitled “ENGLISH TO TAMIL MACHINE TRANSLATION SYSTEM USING PARALLEL CORPUS” was lying in my lap since 2013. I was planning to edit and publish it in book form after making necessary modifications. But as I have taken up some academic responsibility in Amrita University, Coimbatore after my retirement from Tamil University, I could not find time to fulfil my mission. So I am presenting it in raw format here. Let it see the light. Kindly bear with me. I am helpless. Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation. Statistical machine translation (SMT) learns how to translate by analyzing existing human translations (known as bilingual text corpora). In contrast to the Rules Based Machine Translation (RBMT) approach that is usually word based, most mondern SMT systems are phrased based and assemble translations using overlap phrases. In phrase-based translation, the aim is to reduce the restrictions of word-based translation by translating whole sequences of words, where the lengths may differ. The sequences of words are called phrases, but typically are not linguistic phrases, but phrases found using statistical methods from bilingual text corpora. Analysis of bilingual text corpora (source and target languages) and monolingual corpora (target language) generates statistical models that transform text from one language to another with that statistical weights are used to decide the most likely translation.

In large societies like India there is a huge demand to convert one human language into another. Lots of work has been done in this area. Many transfer based MTS have developed for English to other languages, as MANTRA CDAC Pune, MATRA CDAC Pune, SHAKTI IISc Bangalore and IIIT Hyderabad. Still there is a little work done for Hindi to other languages. Currently we are working on it. In this paper we focus on designing a system, that translate the document from Hindi to English by using transfer based approach. This system takes an input text check its structure through parsing. Reordering rules are used to generate the text in target language. It is better than Corpus Based MTS because Corpus Based MTS require large amount of word aligned data for translation that is not available for many languages while Transfer Based MTS requires only knowledge of both the languages(source language and target language) to make transfer rules. We get correct translation for simple assertive sentenc...

Journal of Intelligent Systems, 2018

Building an automatic, high-quality, robust machine translation (MT) system is a fascinating yet an arduous task, as one of the major difficulties lies in cross-linguistic differences or divergences between languages at various levels. The existence of translation divergence precludes straightforward mapping in the MT system. An increase in the number of divergences also increases the complexity, especially in linguistically motivated transfer-based MT systems. This paper discusses the development of Telugu-Tamil transfer-based MT and how a divergence index (DI) is built to quantify the number of parametric variations between languages in order to improve the success rate of MT. The DI facilitates MT in proposing where to put efforts for the given language pair to attain better and faster results. In addition, handling strategies of different types of divergences in a transfer-based approach to MT are discussed. The paper also includes the evaluation method and how an improvization ...

The concept of transfer grammar is not a recent phenomenon. Even in 1954 Harris discusses about the importance transfer grammar in the context of translation, machine translation, language teaching and language learning. We became aware of it recently due to our involvement in machine translation (MT). Presently we are interested in preparing a transfer grammar for English Tamil MT. Harris has proposed an elaborate methodology to prepare a transfer grammar (His idea of transfer grammar has been explained below under the heading “Transfer Grammar”. Here we narrow down our efforts just to correlate syntactic structure of English with that of Tamil from the point of view preparing a transfer grammar for English-Tamil machine translation. For this purpose the computable syntactic structures of English and Tamil have been worked out. These computational syntactic structure analyses are different form ordinary syntactic structure analyses in the sense that the computational syntactic structures are viable for computational processing. A transfer grammar for machine translation has to be prepared using these computational syntactic structure analyses. A transfer grammar is an important component in a machine translation system. This helps us to map one language structure into another language structure. As English and Tamil belongs to two different types of language groups, that is English as predominantly SVO patterned language and Tamil a predominantly SOV patterned language showing unique characteristics which differentiate them drastically from one another, it is possible to manipulate these differences to form transfer rules. These transfer rules can be used to map the English structure into Tamil Structure and vice versa.

2013

Machine Translation is the translation of one natural language into another using automated and computerized means. For a multilingual country like India, with the huge amount of information exchanged between various regions and in different languages in digitized format, it has become necessary to find an automated process from one language to another. In this paper, we take a look at the various Machine Translation System in India which is specifically built for the purpose of translation between the Indian languages. We discuss the various approaches taken for building the machine translation system and then discuss some of the Machine Translation Systems in India along with their features.