Non-cognitive Tools of Translation

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Non-cognitive Tools of Translation

--- Jadhav Kaveri Narayanrao

 Translation is the process of rendering a text from one languageinto another. If we peep into the history of the translated literature, we find

that translation began only after the appearance of written literature. An

important role in history of translation has been played by of religious texts.

Firstly the religious texts were prominently translated. Thereafter, the

scientific literature got the prominence and preference. Due to the demand

of business documentation consequent to the Industrial Revolution that

began in the mid-18th century, shift got changed to legal and business

documents. And at present, in the wake of globalization every genre of

literature is getting translated. As an effect of globalization, different groups

of people are coming together, bringing with them their own culture, values

and ethos. Translation has been playing an important role in bridging the

cultures and making this world a global village in true sense.

In the field of literature, we find that the literature is sustained

by the agency of translation. Translation promotes cosmopolitanism. Edith

Grossman, in her valuable little book Why Translation Matters 1, has shown

how the very notion of literature would be inconceivable without translation.

Profession of the translation is becoming increasingly essential in the era of

globalization. With the increasing business demand, now a day, translation

has been developed as a full-fledged profession and some translation

associations and companies have cropped up with dedicated services. Every

bilingual person does a translation at either mental or explicit level.

Moreover, to show solidarity of the worldwide translation community in an

effort to promote the translation profession in different countries, an

International Translation Day is celebrated on 30th September.  A literary

1

Why Translation Matters , by Edith Grossman, Yale University Press, 2010

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critic  George Steiner in his both seminal2  and controversial work ‘ After

Babel’ has stated that “To understand is to decipher. And to hear is to

translate”3 – thus amplifying the very gamut of the translation and placing it

at the centre of all intellectual processes.

The Scope of the Paper:

 The subject assigned to me for this seminar is ‘Non-cognitive Tools of

 Translation’. Hence, it is imperative to first define the scope of the paper

before beginning with the subject-matter. The word ‘non-cognitive’ indicates

the exclusion of cognitive tools of the translation. Cognition means the

mental faculty of acquiring knowledge by the use of reasoning, intuition, or

perception or the knowledge acquired through these means (i.e. reasoning,

intuition, or perception). Cognitive means concerned with the acquisition of

knowledge viz. by the use of reasoning, intuition or perception or relating to

the thought process. Thus, the cognitive tools incorporate the actual

thought process, knowledge, reasoning, perception, intuition, etc.

 Therefore, the non-cognitive tools indicate the tools other than these, i. e.

other than the actual thought process, knowledge, reasoning, perception,

intuition, etc. and which at the same time are helpful to the acquisition of

knowledge or to the thought process. Thus, the scope of this paper is to deal

with the tools other than the knowledge which are helpful to generate

knowledge and get the translation done. The tools, in themselves, in turn,

are the outcome of the knowledge. These tools can be either tangible or

intangible. 

2A work from which other works grow. The term usually refers to an intellectual or artisticachievement whose ideas and techniques have been adopted or responded to in later works byother people, either in the same field or in the general culture.

3After Babel: Aspects of Language and Translation,  Oxford University Press, UnitedKingdom,1975. He states that all human communication within and between the languages is

translation.

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The Process of Translation :

 To know the tools of translation, we should also know what the process of

translation involves. The Process of translation embraces restatement,

interpretation and transformation. The human translation process involves

two major steps, i.e.

1)  Decoding the meaning of the source text; and

2)  Re-encoding this meaning in the target language.

Behind this apparently simple procedure lies a complex cognitive operation,

i.e. to decode the meaning of the source text (from which he is translating),

the translator has to have a good knowledge of grammar, semantics,

syntax, idioms, etc. of the source language, so that he will be able to

interpret and analyze the text.

1) 

 The translator should also have a command over the target

language (the language into which he is translating) to re-encode the

meaning.

2)  The translator should also have an acquaintance with the subject

matter of the text being translated;

Anything and everything that helps and improves this cognitive process

becomes a non-cognitive tool of the translation.

Non-cognitive Tools of Translation :

1)  Translation does not mean substituting a word of one language by a

word from the other language. It involves creativity, prudence,

skillfulness and much more which an author has to have for a new

literary creation. In this sense, a translator can be equated with a

Sanskrit word ‘ Kavi’. In Sanskrit texts nothing about a translator has

been told. But, texts on poetics have exhaustively dealt with the tools /

resources of a poet which are equally applicable to a translator. Three-

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fold reasons/causes of the Kāvya (EòÉ´ªÉEòÉ®úhÉ ) have been told by the

rhetoricians. They are |ÉÊiɦÉÉ, ´ªÉÖi{ÉÊkÉ   and  +¦ªÉɺɠ.  Though emphasis was

given on |ÉÊiɦÉÉ , the importance of the latter two was equally recognized.

And they were reckoned not as causes but as a cause since all three

produce a good K āvya in unison .4 Rājaśekhara has given eight causes of

the K āvya , which are good health, genius, repetitive study, devotion,

erudition, knowledge of discussion with or of scholars, firm

remembrance, and enthusiasm5.

Moreover, the ancient Indian scholars have laid down eight well-

known methods by which we may learn the meanings of words or the

relation between words and the objects denoted by them. They are as

follows - ¶ÉÎCiÉOɽƠþ ´ªÉÉEò®úhÉÉä{ɨÉÉxÉEòÉä¶ÉÉ{iÉ´ÉÉCªÉÉn  ù́ ªÉ´É½þÉ®úiɶSÉ*

´ÉÉCªÉºªÉ ¶Éä¹ÉÉÊuù́ ÉÞkÉä́ ÉÇnùÎxiÉ ºÉÉÊxÉvªÉÉiÉ& ʺÉrù{ÉnùºªÉ ´ÉÞrùÉ&**

(1) the usages of words by elders, (2) grammar, (3) analogy (4) lexicon, (5)

direct statement of a trust-worthy authority/person, (6) the rest of the

passage in the context (7) explanation (8) the syntactic connection.Besides this, the importance of contextual factors in determining

the exact meaning of an expression has been emphasized by various

writers in India from very early times. Mammaţa has given a list of such

contextual factors that determine the exact meaning of a word in the case

of ambiguous and equivocal expressions. They are as follows- 

ºÉƪÉÉäMÉÉä Ê´É|ɪÉÉäMɶSÉ ºÉɽþSɪÉÈ Ê´É®úÉäÊvÉiÉÉ*

+lÉÇ & |ÉEò®úhÉƠʱÉR  óMÉÆ ¶É¤nùºªÉÉxªÉºªÉ ºÉÊzÉÊvÉ**ºÉɨÉlªÉÇ̈ ÉÉèÊSÉiÉÒ nù  ä¶É& EòɱÉÉä ´ªÉÎCiÉ& º´É®úÉnùªÉ&*

¶É¤nùÉlÉǺªÉÉxÉ´ÉSUä ônä ù Ê´É¶Éä¹Éº¨ÉÞÊiɽä þiÉ´É&**

¶ÉÎCiÉÌxÉ{ÉÖhÉiÉÉ ±ÉÉäEò¶ÉɺjÉEòÉ´ªÉÉt´ÉäIÉhÉÉiÉ *EòÉ´ªÉYÉʶÉIɪÉɦªÉɺÉ

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(1) Sa sarga   (contact) or sa yoga   (association), (2) Viprayoga  

(dissociation), (3) Sāhacarya  (companionship) (4) Virodhitā  (opposition) (5)

Artha   (the purpose served) (6) Prakaraņa   (context of situation) (7) Lińga  

(indication) (8) Śabdasyānyasya sa nidhi  (the vicinity of another word)6. These factors can be applicable to any language and will be helpful to

any translator while fixing the meaning of the words or phrases.

2) Dictionary, Glossary, Thesaurus, Lexicon :

Lexicon is generally be used to mean a Dictionary or a Glossary. But,

these reference books have slightly different denotations. Lexicon is

considered by some to be a more formal word for dictionary. Dictionaries

offer a breadth and depth in defining the words in a language whereas

glossary is a specialized vocabulary with definitions but does not provide

other information about the words. Glossaries are used to define a

precise set of words which make up the vocabulary of a set of people who

were grouped by a common interest, a profession or other circumstance,

such as glossary of medical science, Botany etc. A dictionary is a

reference book consisting of an alphabetically-arranged list of words with

their definitions, as well as any or all of the following forms such as

(spellings), pronunciations, functions (parts of speech), etymologies, and

syntactical and idiomatic uses. Thesaurus on the other hand provides us

with the synonym and antonyms of a word. Descriptive thesaurus also

contains adjectives suitable to the different shades of the meaning and

examples thereof. Unlike a dictionary, a thesaurus entry does not givethe definition of words.

As far as Sanskrit lexicons are concerned we know that the

oldest such collection were arranged according to the meanings of the

words as in the first chapter of the Nighaņtu . Words with the same or

similar meaning were put together and were called synonyms. These

   āvyaprakāśa  of Mamamţa, II.19

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collections later grew into synonymous Kośa -s like Amarakośa,

Halāyudha, Hemachandra   and such like. We also have Kośa -s which

deal with homonyms (anekārtha or nānārthaka ) like Medinīkośa.7   We

may conclude that the Classical Sanskrit Kośa -s were not dictionaries inthe sense that the dictionaries we find today of other languages but they

were mere monolingual thesauruses.

3) Concordance - A concordance is an alphabetical list of the principal

words used in a book or body of work, with their immediate contexts.

Because of the time and difficulty and expense involved in creating a

concordance in the pre-computer era, only works of special importance,such as the Vedas, Bible, Quran or the works of Shakespeare, had

concordances prepared for them. Concordances are frequently used in

linguistics when studying a text. For example:

•  comparing different usages of the same word•  analyzing keywords•  analyzing word frequencies•  finding and analyzing phrases and idioms•  finding translations of sub-sentential elements, e.g. terminology, in

bitexts and translation memories•  creating indexes and word lists (also useful for publishing).

 Thus, concordance can be helpful as an indirect or secondary tool to

the translation in limited extend of the ancient text whose

concordances have been prepared.

Although the basic process of translation has not gone throughsignificant changes in and of itself, it has always been adaptive to new

technologies and has embraced them quite readily.

4) Machine Translation :  It is also known by its abbreviation MT. It is

a sub-field of computational linguistics. Machine translation (MT) is a

process whereby a computer program analyzes a source text and

produces a target text without human intervention. At the basic level, Studies in Historical Sanskrit Lexicography, Mhendale, M.A., Deccan College, 1973  

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MT simply substitutes words in one natural language by words in

another, but that alone usually cannot produce a good translation of a

text since it fails to give a comprehensive and accurate meaning. This

technique is particularly effective in area where formal or formulaiclanguage is used. Thus, machine translation of government and legal

documents is more readily produced than the conversational or literary

texts.  The quality of machine translation can be substantially improved

if the domain is restricted and controlled.  In machine translation a

programme is to be developed that will understand a text as a person

does. Machine translation is essential in a linguistically diverse

country like India. A Brief Survey on Machine Translation in India has

been carried out by Durgesh Rao of National Centre for Software

 Technology. As per his survey two specific examples of high volume

manual translation are translation of news from English into local

languages, and translation of annual reports of government

departments and public sector units among, English, Hindi and local

languages. To ease and facilitate this work, a majority of the Indian

Machine Translation (MT) systems are for English-Hindi translation.

Some of them are as below :

4.1)  Rule-based Machine Translation (RBMT ) : It is also known as

“Knowledge-based Machine Translation” or “Classical Approach” of MT.

It denotes that machine translation systems is based on linguistic

information about source and target languages, basically gathered

from (bilingual) dictionaries and grammars, covering the semantic,

morphological, and syntactic rules of both language. Having input

sentences (in some source language), an RBMT system generates them

to output sentences (in some target language). Anglabharati   software

deals with machine translation from English to Indian languages,

primarily Hindi, using a rule-based transfer approach. Two different

types of rule-based machine translation systems  RBMT) are given.

 They are

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1.1) Transfer Based Machine Translation Systems (RBMT) and

1.2) Interlingual RBMT Systems (Interlingua).

Both transfer-based and interlingua-based machine translation have

the same idea: to make a translation, it is necessary to have anintermediate representation that captures the "meaning" of the original

sentence in order to generate the correct translation. In interlingua-

based MT this intermediate representation must be independent of the

languages in question, whereas in transfer-based MT, it has some

dependence on the language pair involved. A high quality translation

with fair accuracy is possible with this process of translation, although

this is mainly dependent on the language pair in question, for example

the distance and closeness between the two.

4.2) Dictionary-based Machine Translation: Machine translation also

can use a method based on dictionary entries, which means that the

words will be translated as a dictionary does – word by word, usually

without much correlation of meaning between them. Dictionary look-

ups may be done with or without morphological analysis. While this

approach to machine translation is probably the least sophisticated,

 yet it is ideally suitable for the translation of long lists of phrases on

the sub-sentential (i.e., not a full sentence) level.

4.3) Example-based machine translation:  It is essentially a

translation by analogy. It is founded on the belief that people translate

firstly by decomposing a sentence into certain phrases, then by

translating these phrases and finally by properly composing these

fragments into one long sentence. Phrasal translations are translated

by analogy as per the previous translations available in the corpus.

 The main problem arises with the MT when a word has more than one

meaning.  Today there are numerous approaches designed to overcome

this problem. They can be approximately divided into "shallow"

approaches and "deep" approaches. Shallow approaches assume no

knowledge of the text. They simply apply statistical methods to thewords surrounding the ambiguous word. Deep approaches presume a

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comprehensive knowledge of the word. So far, shallow approaches have

been more successful.

Machine translation has many more advantages like quickness,

economic, easy availability, confidentiality, universality, consistency,etc. It is also marred by the disadvantages like lack of exactness,

accuracy, difficulty to deal with ambiguous words, idiomatic

expressions and homonyms. Despite their inherent limitations, MT

programs are used around the world on large scale.

5) Computer-Assisted Translation :

Computer-assisted translation or computer-aided translation, or CAT

is a form of language translation in which a translator uses computer

software to support and facilitate the translation process.

Computer-assisted translation is sometimes also called machine-

assisted, or machine-aided, translation. It is different than Machine

 Translation. It has emerged to salvage the short-comings of Machine

 Translation.   The automatic machine translation systems available

today are not able to produce high-quality translations. Their output

needs to be edited by a human to correct errors and improve phrasing.

Computer-assisted translation (CAT) incorporates that manual editing

stage into the software. It collaborates human and machine

intelligence. This type of technology is widely used amongst

professional translators. Some translation professional service

providers claim their superior service by stating that they don’t provide

MT but CAT. Computer-assisted translation is a broad and imprecise

term and covers a wide range of tools, from simple to the complicated

one. These can include spell checker, grammar checker,  electronic

dictionaries, either unilingual or bilingual, full text search tool, bi-text

aligners, translation memory tool (consisting of a database of text

segments in a source language and their translations in one or more

target languages) and concordance.

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Machine Translation Projects in India8 :

 The machine translation is still in a nascent stage in India. The earliest

attempts in this direction were done in the early nineties. The

prominent among these were the projects at IIT Kanpur, University ofHyderabad, NCST Mumbai and CDAC Pune. The Technology

Development in Indian Languages (TDIL), an initiative of the

Department of IT, Ministry of Communications and Information

 Technology, Government of India, has played an instrumental role by

funding these projects. Since the 90’s, a few more projects have been

initiated. Some efforts from the private sector were also made; by

Super Infosoft Pvt Ltd and more recently by the IBM India Research

Lab. Some major projects are given below :

1) Anglabharati : It is machine added translation system deals with

machine translation from English to Indian languages, primarily Hindi,

using a rule-based transfer approach. It has been applied mainly in the

domain of public health. The project is primarily based at Indian

Institute of Technology, Kanpur and has been funded by Technology

Development in Indian Languages (TDIL), an initiative of the

Department of IT, Ministry of Communications and Information

 Technology, Government of India.

2) Anubharati : It is a recent project at IIT Kanpur, dealing with

template-based machine translation from Hindi to English.

3) Anusaaraka : The focus in Anusaaraka is not mainly on machine

translation, but on language Access between Indian languages. Using

principles of Pāņinian   Grammar (PG), and exploiting the close

similarity of Indian languages. The system has mainly been applied for

children’s stories. The project originated at IIT Kanpur, and later

 Machine Translation in India: A Brief Survey Durgesh Rao  http://

www.elda.org/en/proj/scalla/SCALLA2001/SCALLA2001Rao.pdf & TranslationResources, Services and Tools for Indian Languages Salil Badodekar, http://www.cfilt.iitb.ac.in/Translation-survey/survey.pdf

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shifted mainly to the Centre for Applied Linguistics and Translation

Studies (CALTS), Department of Humanities and Social Sciences,

University of Hyderabad. It was funded by TDIL.

4) MaTra : It is project of Centre for Development of AdvancedComputing (C-DAC), Mumbai. It aims at machine aided translation

from English to Hindi. The focus is on the innovative use of man-

machine synergy. The system breaks an English sentence into chunks,

analyzes the structure and displays the Hindi output. It is primarily

meant for translators, editors and content providers. Currently, it

works for simple sentences, and work is on to extend the coverage to

complex sentences. It has been funded by TDIL.

5) Mantra : (MAchiNe assisted TRAnslation tool) Mantra translates

English text into Hindi in a specified domain of personal dministration,

specifically gazette notifications, office orders, office memorandums

and circulars. The MANTRA Technology is being expanded to translate

English texts into other Indian languages such as Gujarati, Bengali,

and Telugu. It is developed by Centre for Development of Advanced

Computing (C-DAC), Bangalore.

6)UNL-based MT between English, Hindi and Marathi:

 The Universal Networking Language (UNL) is an international project of

the United Nations University, with an aim to create an Interlingua for

all major human languages. IIT Bombay is the Indian participant in

UNL, and is working on MT systems between English, Hindi and

Marathi using the UNL formalism. This essentially uses an inter-

lingual approach—the source language is converted into UNL using an

‘enconverter’, and then converted into the target language using a

‘deconverter’.

7) English-Hindi MAT for news sentences : The Jadavpur University,

Kolkata has recently worked on a rule-based English-Hindi MAT for

news sentences using the transfer approach.

8) Anuvadak English-Hindi software: Super Infosoft Pvt Ltd is one ofthe very few private sector efforts in MT in India. They have been

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working on asoftware called Anuvadak, which is a general-purpose

English-Hindi translation tool that supports post-editing.

9) English-Hindi Statistical MT : The IBM India Research Lab, New

Delhi has recently initiated work on statistical MT between English andIndian languages, building on IBM’s existing work on statistical MT.

Conclusion :

I have reckoned here some of the tools or resources needed and

available to a translator for accomplishing the task of translation. Each

of these has its own scope within which it renders the help to a

translator. Hence, mere one resource or tool will not suffice to gain the

desired result. Besides, each tool has its own advantages and

limitations. To overcome the limitation of one tool, we have to resort to

the other one. Moreover, we should not forget that these are non-

cognitive tools i.e. of secondary nature and translation being an

intellectual endeavour its main thrust is on cognitive tools. I am

reminded of a quote of Mammaţa that all these cognitive and non-

cognitive tools form one ‘ hetu ’ and the not ‘ hetava ’.

A lot of research is going on in the area of Natural Language

Processing (NLP) and a number of machine translation systems have

been developed and regular efforts are being done for their

improvements. It is a call of the time that Sanskrit scholars get

adaptive to it for the dissemination of knowledge preserved in Sanskrit

language.

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