Filetype PDF | Posted on 22 Sep 2022 | 3 years ago
Authentication
digital resources
131x Filetype PDF File size 0.20 MB Source: www.ijettcs.org
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 9, Issue 5, September - October 2020 ISSN 2278-6856
Systematic Review on Techniques of Machine
Translation for Indian Languages
1 2 3
Simarn N. Maniyar , Sonali B. Kulkarni , Pratibha R. Bhise
1
Departemet of Computer Sciece and Information Techonology
Dr.Babasaheb Ambedker Marathwada University,Auragabad.
2
Departemet of Computer Sciece and Information Techonology
Dr.Babasaheb Ambedker Marathwada University,Auragabad.
3
Departemet of Computer Sciece and Information Techonology
Dr.Babasaheb Ambedker Marathwada University,Auragabad.
Abstract: Machine Translation is the branch of Natural linguistics (CL) and Natural Language Processing (NLP)
Language Processing, which deals the use of Machine Translation that explores the use of computer/mobile application to
software to convert from one language to other natural languages translate text or speech from one natural language known as
with the help of machine Translation Approaches. The objective is the source language to another known as target language.
to fill the language gap between two different languages speaking Like translation done by human, MT does not simply
people, communities or countries. In India we have multiple and substituting words but the application of complex linguistic
greatly distinct language scripts, hence scope of need of language knowledge; morphology, grammar, meaning all this things
translation. In this purpose we present Literature survey on is taken into consideration. Generally, MT is classified into
machine translation on the current scenario of research machine various categories: Direct based, rule-based, corpus based,
translation in India. There is various Machine Translations statistical-based, hybrid-based, example-based, knowledge-
system. Machine translation is considered an important task that
can be used to attain information from documents written in based, principle-based, and online interactive based
different languages. In this paper examine the rule based machine methods. At present, most of the MT related research is
translation is useful for translate for English to Indian languages. based on Rule based approaches because rule based is
always extensible and maintainable. Morphological
Keywords: NLP, Machine Translation, Techniques, analysis, part of speech tagging, chunking, parsing and
RBMT word sense disambiguation this is the major goals of
1. INTRODUCTION machine translation. In this paper, we are present a
systematic literature review of the techniques used for
Natural language processing can be classified as a subset of machine translation. We have also mentioned recent work
the broader field of speech and language processing. in the table from.
Because of this, NLP shares similarities with parallel 2. Literature Review
disciplines such as computational linguistics, which is
concerned with modeling language using rule-based Table 1: Review for Machine Translation Techniques
models. The goal of natural language processing is to build
computational models of natural language for its analysis SR. PAPER NAME AUTHER YE LAGUA TECHNI
and generation. First, there is technological motivation of NO AR GE PAIR QUES
building intelligent computer system such as machine 1 TRANSLATION OF DR. 2014 TELU RULE-
translation system natural language interfaces to databases, TELUGU- SIDDHARTHA GU- BASED
MARATHI AND GHOSH, MAR AND
man-machine interfaces to computer in general, speech VICE-VERSA SUJATA ATHI STATIST
understanding system, text analysis and understanding USING RULE THAMKE ICAL-
system, computer aided instruction systems, systems that BASED MACHINE AND KALYANI BASED
read and understand printed or handwritten text. Second, TRANSLATION U.R.S
2 RULE BASED NAILA ATA, - ENGLI RULE
there is a cognitive and linguistic motivation to gain better ENGLISH TO BUSHRA SH TO BASED
insights into how humans communicate using natural URDU MACHINE JAWAID, AMIR URDU MACHIN
language. The tools of work in NLP are grammar TRANSLATION KAMRAN E
formalisms, algorithms for representing world knowledge, TRANSL
ATION
reasoning mechanisms, etc. Natural language interfaces to 3 TRANSLATION OF PROF. 2014 ENGLI RULE
databases, natural language interfaces to computer, SIMPLE ENGLISH GORAKSH SH TO BASED
question answering system, story understanding and INTERROGATIVE V.GARJE, MARA MACHIN
machine translation system these all the application of SENTENCES TO MANISHA THI E
MARATHI MARATHE, TRANSL
natural language processing. We are focusing in the SENTENCES URMILA ATION,
machine translation application and these approaches. ADSULE TRANSF
Machine Translation (MT) is the field of computational ER MT
4 HYBRID 2018 MARA HYBRID
Volume 9, Issue 5, September - October 2020 Page 44
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 9, Issue 5, September - October 2020 ISSN 2278-6856
MACHINE PROF. THI MACHIN TECHNIQUE
TRANSLATION ABHAPATHAK TO E 15 WEB BASED VISHAL GOYAL 2010 HINDI DIRECT
FROM MARATHI , ANCHAL ENGLI TRANSL HINDI TO AND GURPREET TO TRANSL
TO ENGLISH: A KUMARI SH ATION PUNJABI SINGH LEHAL PUNJ ATION
RESEARCH , AKANKSHA MACHINE ABI
EVOLUTION IN PRASAD TRANSLATION
MACHINE , ASHWINI SYSTEM
TRANSLATION TOPRE 16 A HEURISTIC PRIYANKA 2017 ENGLI GRAPH
, APPROACH FOR MALVIYA, SH TO BASED
RUTUJALONDH GRAPH BASED GAURI RAO, HINDI APPROA
E MACHINE ROHINI B. CH
TRANSLATION JADHAV,
5 TRANSMUTER: G V GARJE, G 2014 ENGLI RULE MAYURI H.
AN APPROACH K KHARATE, SH TO BASED MOLAWADE
TO RULE-BASED HARSHAD MARA MACHIN 17 DEVELOPMENT K. 2015 TELU TRANSF
ENGLISH TO KULKARNI THI E OF TELUGU- PARAMESWARI GU- ER-
MARATHI TRANSL TAMIL TAMI BASED
MACHINE ATION TRANSFER- L MACHIN
TRANSLATION BASED MACHINE E
6 MARATHI TO G.V. GARJE, 2016 MARA RULE TRANSLATION TRANSL
ENGLISH PHD, AKSHAY THI BASED SYSTEM: WITH ATION
SENTENCE BANSODE, TO TRANSL SPECIAL SYSTEM
TRANSLATOR SUYOG ENGLI ATION REFERENCE TO
FOR SIMPLE GANDHI, ADITA SH APPROA DIVERGENCE
ASSERTIVE AND KULKARNI CH INDEX
INTERROGATIVE 18 AN ENGLISH TO R.M.K. SINHA, 2003 ENGLI RULE-
SENTENCES HINDI MACHINE- A. JAIN SH TO BASED
7 ENGLISH TO G.V. GARJE, 2015 ENGLI RULE AIDED HINDI AND
MARATHI RULE- G.K. KHARATE SH TO BASED TRANSLATION EXAMPL
BASED MACHINE AND M.L. MARA MACHIN SYSTEM E-
TRANSLATION OF DHORE THI E BASED
SIMPLE TRANSL 19 ENGLISH–HINDI ONDŘEJ BOJAR, 2008 ENGLI RULE-
ASSERTIVE ATION TRANSLATION IN PAVEL SH TO BASED
SENTENCES 21 DAYS STRAŇÁK, HINDI AND
8 NEURAL SANDEEP SAINI, 2018 ENGLI NEURAL DANIEL ZEMAN EXAMPL
MACHINE VINEET SH TO MACHIN E-
TRANSLATION SAHULA HINDI E BASED
FOR ENGLISH TO TRANSL
HINDI ATION SIMPLE ANANTHAKRIS
9 NEURAL KARTHIK 2017 INDIA NEURAL 20 SYNTACTIC AND HNAN 2008 ENGLI STATIST
MACHINE REVANURU, N MACHIN MORPHOLOGICA RAMANATHAN, SH- ICAL
TRANSLATION OF KAUSHIK LANG E L PROCESSING PUSHPAK HINDI MACHIN
INDIAN TURLAPATY, UAGE TRANSL CAN HELP BHATTACHARY E
LANGUAGES SHRISHA RAO S ATION ENGLISH-HINDI YA, JAYPRASAD TRANSL
10 NEURAL HIMANSHU - ENGLI NEURAL STATISTICAL HEGDE, RITESH ATION
MACHINE CHOUDHARY, SH- MACHIN MACHINE M.
TRANSLATION ADITYA TAMI E TRANSLATION SHAH,SASIKUM
FOR ENGLISH- KUMAR L TRANSL AR M
TAMIL PATHAK ATION 21 EXAMPLE BASED F.H.A.M. 2015 ENGLI EXAMPL
11 STATISTICAL SUBALALITH, 2018 ENGLI STATIST MACHINE SILVA , SH- E
MACHINE AARTHI SH TO ICAL TRANSLATION A.R.WEERASIN SINHA BASED
TRANSLATION VENKATARAMA HINDI MACHIN FOR GHE AND LA MACHIN
FROM ENGLISH N,BASIMSHAHI E ENGLISH- H.L.PREMARAT E
TO HINDI DBAQUI TRANSL SINHALA ENE TRANSL
ATION TRANSLATIONS ATION
12 G.SURYAKALA 2018 ENGLI STATIST 22 A NOVEL PROF.KRUSHNA 2014 ENGLI EXAMPL
MACHINE ESWARI, SH TO ICAL . APPROCH FOR DEO.T.BELERA SH TO E-
TRANSLATION N.V.S.SOWJAN HINDI MACHIN INTERLINGUAL O, MARA BASED
FROM ENGLISH YA,P.SURYAPR E EXAMPLE-BASED PROF.VINOD. S. THI MACHIN
TO HINDI ABHAKAR RAO TRANSL TRANSLATION OF WADNE E
ATION ENGLISH TO ,PROF. S. V. TRANSL
13 ETRANS- PROMILA 2012 ENGLI RULE MARATHI PHULARI, ATION
ENGLISH TO BAHADUR, SH TO BASED PROF. B.S.
SANSKRIT A.K.JAIN, SANS MACHIN KANKATE
MACHINE D.S.CHAUHAN KRIT E 23 DESIGN AND LATHA R NAIR, 2012 MALA A
TRANSLATION TRANSL DEVELOPMENT DAVID PETER YALA TRANSF
ATION OF A S, RENJITH P M TO ER
14 TRANSFORMATIO MR.UDAY C. 2012 ENGLI RULE MALAYALAM TO RAVINDRAN ENGLI BASED
N OF MULTIPLE PATKAR, SH TO BASED ENGLISH SH APPROA
ENGLISH TEXT PROF.PRAKASH SANS MACHIN TRANSLATOR- A CH
SENTENCES TO R. DEVALE, KRIT E TRANSFER
VOCAL SANSKRIT PROF.DR.SUHA TRANSL BASED
USING RULE S.H.PATIL ATION APPROACH
BASED 24 MALAYALAM TO ANISREE P G1, 2016 MALA HYBRID
Volume 9, Issue 5, September - October 2020 Page 45
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 9, Issue 5, September - October 2020 ISSN 2278-6856
ENGLISH RADHIKA K T2 YALA APPROA 3.1 Rule Based Translation
MACHINE M TO CH A Rule-Based Machine Translation (RBMT) system
TRANSLATION: A ENGLI consists of collection of various rules, called grammar
HYBRID SH rules, a bilingual lexicon or dictionary, and software
APPROACH
25 A HYBRID NITHYA B, 2013 ENGLI HYBRID programs to process the rules. Rule-Based Machine
APPROACH TO SHIBILY JOSEPH SH TO APPROA Translation (RBMT), also known as Humaistic methods of
ENGLISH TO MALA CH MT, is a general term that indicates machine translation
MALAYALAM YALA systems based on linguistic information about source and
MACHINE M
TRANSLATION target languages basically recover from (bilingual)
dictionaries and grammars covering the main semantic,
26 A RULE BASED T.K. BIJIMOL1* 2018 MALA A RULE morphological, and syntactic consistencies of each
APPROACH FOR , JOHN T. YALA BASED language respectively. Having source language sentences ,
TRANSLATION OF ABRAHAM2 M TO APPROA
CAUSATIVE ENGLI CH an RBMT system generates them to target language
CONSTRUCTION SH sentences on the support of morphological, syntactic, and
OF ENGLISH AND AND advantages of RBMT system is that the interlingua develop
MALAYALAM ENGLI into more valuable as the amount of target languages it can
FOR THE SH TO
DEVELOPMENT MALA be turned into development. Interlingual machine
OF YALA translation system has been built operational at the
PROTOTYPE FOR M economical level is the KANT system (Nyberg and
MALAYALAM TO Mitamura, 1992), which is develop to translate Caterpillar
ENGLISH AND
ENGLISH TO Technical English (CTE) into other languages. The
MALAYALAM interlingual technique is clearly attractive for multilingual
BILINGUAL systems. All other analysis modules and of all generation
MACHINE modules both of analysis module can be independent.
TRANSLATION
SYSTEM 3.2 Direct Translation:
27 RULE BASED R. REMYA, S. 2009 ENGLI RULE One of the simplest machine translation techniques is
MACHINE REMYA, R. SH TO BASED Direct Machine Translation in which technique with the
TRANSLATION REMYA, AND K. MALA MACHIN help of bilingual dictionary direct word to word translation
FROM ENGLISH P. YALA E is done. Starting with the shallowest level at the bottom of
TO MALAYALAM SOMAN M TRANSL
ATION the pyramid is the Direct Machine Translation Technique.
DMT technique is the oldest technique and also less
popular technique. Direct translation is made at the word
3. Overview of machine translation level. Machine translation systems that use this approach
Approaches are capable of translating a language, source language (SL)
Researchers proposed many approaches for the Machine directly to target language (TL). Words of the source
Translation. Overview of main approaches is presented language are translated without passing through an
here. There are two broad categories of Machine additional/intermediary representation. The analysis of
Translation Systems, namely Rule-Based and Empirical source language texts is oriented to only one target
Based Machine Translation Systems. Hybrid Machine language. Direct translation systems are basically bilingual
Translation system takes the benefits from both Rule-Based and uni-directional. Direct translation technique needs only
Machine Translation System and Empirical Based Machine a little syntactic and semantic analysis. SL analysis is
Translation System. Rule-Based Machine Translation oriented specifically to the production of representations
System is further classified into Direct, Transfer and applicable for one particular Target language . DMT is a
Interlingua, while Empirical Based Translation System is word-by-word translation technique with some simple
classified into Statistical and Example based machine grammatical adjustments.
translation system
Figure 2: Direct Machine translation Approach
Figure1. Technique of Machine Translation
Volume 9, Issue 5, September - October 2020 Page 46
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com
Volume 9, Issue 5, September - October 2020 ISSN 2278-6856
3.3. Statistical-based Approach: example-based machine translation is the idea of translation
SMT can be described as the process of finding and by analogy. The ethic of translation by analogy is encoded
matching identical pairs from SL and TL in parallel to example-based machine translation through the example
corpora. The goal of SMT is to make an optimal decision in translations that are used to train such a system.
language translation by using statistical decision theory, 3.5. Knowledge-Based MT:
based on probability distribution function. The important Knowledge-Based Machine Translation (KBMT) requires
feature of SMT is the presence of statistical table, which complete understanding of the source text prior to the
can be built by using supervised or unsupervised statistical translation into the target text. KBMT is implemented on
machine learning algorithms. Statistical table generally the Interlingua 56 International Journal on Natural
contains statistical information pertaining to sentences or Language Computing (IJNLC) Vol. 4, No.2,April 2015 57
languages. SMT relies on a statistical calculation of the architecture. KBMT must be supported by world
probabilities of a match [17] by using two probabilistic knowledge and by linguistic semantic knowledge around
models: Language model and Translation model, rather meanings of words and their combinations.
than relying on linguistic translation algorithms. The idea
of SMT is that document can be translated on the basis of 3.6. Hybrid-based Translation:
probability distribution function P(t/s), where P(t/s) is the Hybrid-based technique is develop using the advantages of
probability of translating a sentence, say ’s’ in SL to a SMT and EBMT methodologies. The hybrid technique
sentence ’t’ in TL. And this function is generated easily by used in a number of different ways. Translations are
using Bayes theorem. In Bayes theorem probability performed in the first level using a rule-based technique
distribution p(t/s) is obtained from the product of P(s/t) and which is followed by adjusting or correcting the output
p(t), where P(s/t) is the probability that the source sentence using statistical information. other way in which rules are
is a translation of the target sentence, and P(t) is the used to pre-process the input data and for post-process the
probability of the TL statistical output of a statistical-based translation system.
By taking the advantage of both statistical and rule-based
translation methodologies, a new approach was developed,
called hybrid-based approach, which has confirm to have
better efficiency in the area of MT systems. Now days,
several governmental and private based MT fields use this
hybrid-based technique to develop covert from source to
target language, which is based on both rules and statistics.
The hybrid technique can be used in different ways. In
some cases, translations are performed in the first level
using a rule-based technique followed by adjusting or
correcting the output using statistical information. Other
way, rules are used to pre-process the input data as well as
post-process the statistical output of a statistical Machine
translation system. Hybrid based technique is better than
the previous and has more power, flexibility, and control in
translation. Hybrid technique integrating more than one MT
paradigm are receiving increasing attention. The METIS-II
MT system is an example of hybridization about the EBMT
Figure 3: Statistical Machine Translation Approach framework; it avoids the current need for parallel corpora
by using a bilingual dictionary (similar to that found in
3.4. Example-based translation: most RBMT systems) and a monolingual corpus in the TL
Basic idea of this MT is to reuse the examples of already (Dirix et al., 2005). An example of hybridization about the
existing translations. An example-based translation is uses rule-based paradigm is given by Open. It integrates
a bilingual corpus as its main knowledge base and it is statistical methods within an RBMT system to choose the
essentially translation by analogy. Example-based machine best translation from a set of competing hypotheses
translation (EBMT) is define by its use of bilingual corpus (translations) generated using rule-based methods.
with parallel texts as its main knowledge, in which
translation by analogy is the main idea. An EBMT system 3.7. Neural Machine Translation:
is given a set of sentences in the source language (from Neural Machine Translation is an approach to MT that uses
which one is translating) and corresponding translations of a neural network which directly models the conditional
each sentence in the target language with point to point probability of translating a given source sentence to a target
mapping. These examples are used to covert similar types sentence.
of sentences of source language to the target language.
Example acquisition, example base and management, 4. Conclusion:
example application and synthesis this is four tasks in In this paper explain the various standardized approaches in
Example based machine translation system. At the base of the field of Machine translation word wide and especially
Volume 9, Issue 5, September - October 2020 Page 47
no reviews yet
Please Login to review.
