Survey on Machine Translation systems for Ancient Indian Languages

Survey on Machine Translation systems for Ancient Indian Languages

Sreedeepa H. S

Assistant Professor Computer Science & Engineering

College Of Engineering Kallooppara Thriuvalla

Divya Madhu

Assistant Professor Computer Science & Engineering

Vidya Academy of Science & Technology Technical Campus Kilimanoor

Abstract Sanskrit is a less ambiguous, language suitable for natural language processing. Most of the ancient In-dian books were written Sanskrit. This paper is a survey done on different Sanskrit involved machine translation systems.

Keywords Machine translation; Interlingua; source lan- guage; direct translation; destination language.

1. INTRODUCTION

   Sanskrit is a less ambiguous language. As its less am- biguous in nature it is more suitable for natural language pro- cessing. [1] Sanskrit is a free word order language. Sanskrit, considered as the mother of most of all languages, possesses a rich grammar which was developed by Panini around 3000 years ago and it includes 3,959 rules. NASA, the most ad- vanced research center in the world has discovered that San- skrit is the less ambiguous spoken language on the planet. There is saying that Sanskrit is the best suitable language for computers. Due to the unambiguous nature of the language Sanskrit is the simplest language that is most suited for Artifi- cial Intelligence and Natural Language Processors.

   Machine translation (MT) is the process of converting one natural language to another using application software. Mainly there are three types of rule based machine translation tech- niques- direct approach, transfer based approach and interlin- gua based approach. Most of the translators developed were concern about word translation, bilingual dictionaries based on direct translation.

2. MACHINE TRANSLATION SYSTEMS Machine translation (MT) is the process of converting sen-

tences in one natural language called source language to an- other called destination language. One of the the major classi- fication of machine translation approach include Rule based machine translation, Statistical, Example-based, Hybrid ma- chine translation and Neural machine translation. In rule based approach large set of rules are manually developed and apply these rules to map structures from source to target language TABLE I.[2] summarizes the advantages/disadvantages of ma- jor machine translation approaches.

I. MACHINE TRANSLATION APPROACHES

Direct translation, transfer based and interlingua based ap- proaches are the major rule based machine translation tech- niques.

1. Direct Translation

   Direct translation is the simplest form translation in which words in the source sentence are directly converted into a destination language .In this translation is done with the help of a bilingual dictionary. Word by word translation is performed here. Anusaaraka is an example of direct ta- ranslation based well known machine translation system.

2. Transfer based Translation

   A database of translation rules is used to translate a text in source language to target language. In this approach whenever a sentence is matched to any one of the rules present in the

   database its directly translated using a dictionary. The diction- ary is such as source language(SL) dictionary, target lan- guage(TL) dictionary, and a bilingual dictionary. There are mainly two steps in this approach, syntactic transfer and se- mantic transfer.

   In syntactic transfer the SL sentence is analysed to generate asyntactic structure called parse tree and this parse tree of SL is then transfers to TL parse tree. At semantic transfer analyse a SL input to a language specific semantic representation and transfer this to TL semantic representation. Case frames and logical forms are the two constructs used for semantic repre- sentation. Finally, these representations are used generate syn- tactic structure and then surface sentence in the TL.

3. Interlingua based Translation

   In Interlingua based approach a language independ- ent frame work is developed for translation of source lan- guage to destination language. [2] The interlingua approach has a number of advantages. It requires fewer components for the translation of the source language to each target language, and to add a new language. It allows both the analyzers and generators to be written by monolingual system developers. Also, it can handle languages that are different from each other.

   DeryleW. Lonsdale, Alexander M. Franz, and John

   1. R. Leavitt presented the design and development of an in- terlingua for a large-scale MT project, 1SL-nTL. They also discussed how the resulting Knowledge-based, Accurate Nat- ural-Language Translation (KANT) interlingua handles complexity,and development of different stages efficiently. It is developed in a balanced fashion with maximal coverage. They use, a recursive list-based structural representation of source sentences in this approach. An interlingua frame con- sists of a head concept, feature-value pairs, and semantic slots. It may contain nested interlingua frames. The source language expressions and semantic units from the domain were considered for the concept generation. The overall for- mat is modeled using frame-based structures. The f-structure reflects deep semantic relationships between major constitu- ents. [8]

      The Interlingua approach is based on the concept that MT must go beyond purely linguistic information, syntax and semantics, and should understand the content of texts. Interlingua based translation is divided into two monolingual components: ana- lyzing the source language text into an abstract universal lan- guage-independent representation of meaning, the interlingua, and generating this meaning using the lexical units and the syn- tactic constructins of the target language. [9]

      Fig.1 represents the vaquous triangle for machine transla- tion approaches. It depicts the three types of rule based trans- lation system. The main phases present in the translation are analysis transfer and generation phase.

      1. .Vaquous triangle for machine translation III RELATED WORKS

A detailed study on machine translation system on Sanskrit, Interlingua based machine translation system and Paninian framework for translation were done in developing the proposed system. Akshar Bharathi et.al. provided details of the Paninian framework [1], Parsing Free Word Order Lan- guages in the Paninian Framework [2], and Karaka analysis [3]. He also explains the use of lexical functional grammar (LFG) in unification for specifying mapping to grammatical relations[4]. The parsing of Sanskrit sentences using LFG is explained by Mrs. Namrata Tapaswi et.al. [5]. Paul Kiparsky gives detailed description of different levels of Paninian framework with examples and rules of Ashtadhyayi and rule formation on different levels of Paninian framework. [6] Sudhir Kumar Mishra et.al. [7] gives a detailed study on the Karaka analysis system based on rules of Ashtadhyayi with examples.

Sameh AlAnsary et.al. briefly reviews three of the most renowned interlingua-based machine translation projects, Distributed Language Translation (DLT), UNIversal TRANs- lator(UNITRAN) and KANT system. DLT, a research project developed in Utrecht, The Netherlands, is an interactive sys- tem developed to operate over computer networks. Translation is distributed between two independent terminals; one for the analysis and another for generation.UNITRAN is a translation system developed at Massachusetts Institute of Technology. The system operates bidirectionally between Spanish and Eng- lish. KANT system has been developed at Carnegie- Melon University (CMU) in Pennsylvania, USA in 1989. KANT is the only interlingua-based MT system to be operational com- mercially. It has been used in translating English technical doc- uments into French, Spanish and German.

Translation system developed JNU uses word sense disambig- uation module and Anaphora Resolution module Here they used Sanskrit as SL and Hindi as TL. Sanskrit to English ma- chine translation developed by Subramanian focus on sandhi vicheda,,and morphological analysis.

IV SANSKRIT INVOLVED MACHINE TRANSLATION SYSTEMS

Some of the Sanskrit involved machine translation systems were shown in the TABLE II.[3]. Most of the systems were developed on the rule based approach.

TABLE II MACHINE TRANSLATION SYSTEMS DEVELOPED FOR SANSKRIT

V. CONCLUSION

Linguistic studies on Sanskrit are less compared to other Indian natural languages Rule based translation scheme is used in most of the Sanskrit involved translation systems Most of the systems were developed either in direct or transfer based approaches and for simple sentences. Very rare translation systems uses Sanskrit as source language. There is an interesting and more efficient machine transla- tion system developed based on interlingua approach . As Sanskrit considered as mother of many Indian languages a translation system based on interlingua approach seems to be more efficient and useful.

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