the motivation- statements by prof raj reddy information will be read by both humans and machines -...

The Motivation-Statements by Prof Raj Reddy

• Information will be read by both humans and machines - more so by machines.

• If you are not in Google you are not there !

What does Google do ?

The Google

• It removes the stop words

• It stems

• It does not disambiguate

• It makes you wonder why you did such a beautiful Translation

The machine Translation

• Often follow the Law of Diminishing Returns – Asymptotic –

• Require Huge Human, material and computer resources.

• Assume that the user is unaware of either the context or has no intelligence

• Almost impossible to attain perfect Human Like translation

• Lexical, syntactical and semantic error

Our Experience

• The Migrant workers in India pick up the local alien language in less than a month-

• The Butler English

• Learning by Experience and usage

What is a Good Translation• If the user does not get irritated when reading

the translated text

• Intelligent Human Beings have more resistance to irritation

• If we design a Machine Translation system that assumes Intelligent Users, then the resourses and time required would be significantly less

• Intelligent users would be more tolerant to syntactical and semantic errors

Good Enough Translation• Lexical Errors are easy to handle since bilingual

dictionaries can be built easily• Mike Shamos’s concept of Universal Dictionary and

Disambiguation• Colocation Frequencies have been exploited by us in

Automatic Summarization- Its manifestation is the Phrase dictionary

• Add to this simple aligned corpora of human translated frequently used sentences

• Mine the sentences for new phrases and mine the phrases for new words

• Use the Wikipedia Approach to enhancing the learning• First prototype built by Hemant, Madhavi, Raj and me for

Hindi• Later on extended to Kannada and tamil by Rashmi,

Sravan, Sheik, Anand, Vivek and Vinodini• Now we have the ability to make EBMT good enough in 30

Days

Universal Dictionary

• Mike Shamos’s contribution to UDL• A collection of dictionaries in various languages.

• Contains many European languages. • Given a word in English we can get the meaning

of the word in various languages at one click

• A total of five Indian languages were added to the Universal dictionary:

• Kannada, Telugu, Tamil, Malayalam, Hindi

• Microsoft Access Database.

Good Enough Translation

Example Based Machine Translation A good enough Translation

• Requires:– A set of sentences in the source language and

their corresponding translation in the target language.

– A set of phrases in the source language and their corresponding Translation in the target language

– A Bilingual Dictionary (English-Hindi) • It looks for the longest match to learn• The best part of the EBMT system we have is

that it keeps on learning day by day• Right now available for -

English-Hindi-Telugu-Kannada-Tamil

Problem statement

Aim:• To obtain a “good enough” translation

Constraints:

• Limited Data• Limited Processing Time

Languages Words Phrases Sentences

Hindi 33947 3800 34000

Kannada 33000 12480 40000

Malayalam 6470 - -

Tamil 52400 21613 48295

Telugu 15782 - -

Corpora Table (Indian Languages)

Languages

Co

rpo

raCorpora Table (Indian Languages)

G-EBMT

• Similar examples are tokenized to show equivalence classes and stored as a generalized example. [Brown 99]

• A database of sentence and phrase rules + bilingual dictionary

Format:

Source sentence rule Target sentence ruleSource phrase rule Target phrase rule

she brought a <noun> aval’u <noun> than’dal’u

Input sentence : she brought a dog Output sentence : aval’u naayi tandal’u

Root words take different forms according to its meaning in the sentence, i.e. the sequential order of the words in the sentence does not become important, unlike in English, for example ,

-I am Going Home-

-Home Going I am

May mean the same thing in Indian Languages

Word Order Free – A feature of Indian languages

The Great War of Mahabharat took place between the Pandavas and the Kauravas.

Krishna told Yudhisthira that Drona would finish his entire army if not checked.

The only way to check Drona was to make him lay down his arms.

That was possible only when Drona was told that his son Ashwathama had died.

Telling a lie is not a good practice even in wars. Krishna came up with an idea, Yudhisthira agreed reluctantly.

Bhima killed an elephant named ‘ Ashwathama '.

Then he loudly announced for all to hear,

‘Ashwathama Hathah Kunjarah’Ashwathama– an elephant, is killed

Lord Krishna blows his conch and makes the word Kunjarah (elephant) inaudible in the battlefield.

Drona turned to Yudhisthira and asked if that was true.

Yudhisthira said, “Ashwathama is killed - An Elephant ”

he added in a low voice

Dronacharya hears only the first two words

‘Ashwathama Hathah’, Presumed that his son ‘Ashwathama’ has been killed.

He gives up his weapons and sits in prayer.

Dhristadymna takes advantage of the opportunity and kills Dronacharya.

This story depicts Indian Languages like Sanskrit are Word Order Free Languages-

Hence good lexical Corpora would help in making nearly good enough Translation

Generalization for Indian Languages-Phrase level vs.

Sentence level• Should we generalize

– at the phrase level? – at the sentence level?

Input English Sentence : they did not follow the rules

Phrase Level Generalization:

<pron> did not <pron> maadalilla they did not avaru maadalilla

follow the <noun> <noun> annu paalisi follow the rules niyamagal’l’ annu paalisi

Output sentence: avaru maadalilla niyamagal’l’annu paalisi (BLEU score : 0)

(Word order is very important)

Sentence Level-Generalization:<pron> did not follow the <noun> <pron> <noun> annu paalisalillaOutput sentence: avaru niyamagal’l’annu paalisalilla (BLEU

score : 1)

Motivation for Linguistic Rules

What happens if the input sentence doesn’t match with any of the rules ?

WHEN: Will surely happen since we can’t have an infinite set of examples…..

WHAT TO DO: The most obvious thing - go in for… Word-Word Translation as back-off

– which is not a good idea as we will need to rearrange the words

Can we add Linguistic rules?

When will this happen?

What do we do if it does?

Proposed Method

Managing Idioms

• Meanings of idioms cannot be inferred from the meanings of the words that make it up

• Idioms are stored separately in a file in the following format

bite the hand that feeds

un'd'a manege erad'u bage

Applying Language Specific Rules

Stage1: Tagger and Stemmer (1)

Input sentence: he is playing in my house

Output of the tagger *:

he _PP is_VBZ play_VBG in_IN my _PP$ house _NN

PP- Personal pronounVBZ-verb, 3rd person singular presentVBG-verb, gerund or present participleIN-Preposition or subordinating conjunctionPP$-Possessive pronounNN-Noun, singular or mass

*Helmut Schmid, “Probabilistic Part-of-Speech Tagging using Decision Trees”, Proceedings of International Conference on New Methods in Language Processing, September 1994.

Stage1: Retagging (2)Why?

Auxiliary verbs are different from other verbs in Indian Languages

he_PP is_auxv play_VBG in_IN my _PP$ house_NN

Stage2: Splitting based on Prepositions and Conjunctions and Reordering (1)

Original sentence: with prepositionhe is playing in my house

P1: in my houseP2: he is play

Reordering the preposition (postposition) :P1: my house in

P2: he is play

Original sentence: with conjunction AND connecting two PP’s

She is free to develop her ideas and to distribute itE1: She is freeE2: develop her ideas toE3: andE3: distribute it to

Special case: two or more prepositions

I am going to school with Mary

After splitting and Reordering:

P1: Mary with (Mary jote)

P2: school to (shaale ge)

P3: I am go (naanu hooguttidene)

Mary jote shaalege naanu hooguttidene

Stage2: Splitting based on Prepositions and Conjunctions and Reordering (2)

Stage3: Reorder Interrogations and Verbs

Place the verbs at the end:

P1: my house inP2: he is play (play is placed at the end of P2)

If a verb and a particle are present together in any of the parts, place the verb along with the particle at the end of that part – Explained in Stage 5

Stage 4: Reorder Auxiliary/Modal verbs

P1: my house in

P2: he play is (is is placed at the end of P2)

Special Case:

He is not playing in my house

E1: my house in

E2: he play is not

Stage5: word-word translation(1)

Dictionary format: english-word category kannada-word

Sample: in IN alli in RB o’lage to IN ge (if “to” is followed by a noun) to IN alu put on V-P haakiko (where, RB- adverb, V-P-verb particle)

Join the parts (P1 and P2) obtained from Stage 4, and translate word to word.

my house in he play is

nanna mane alli avanu aad’u ide

Actual Kannada translation: nanna mane alli avanu aad’ uttiddaane

Stage5: word-word translation(2)

Special care needs to be taken with sentences containing particles:

verb and particle have to be translated together,

Hence an entry in the dictionary is made as,

put on V-P haakiko

Eg:

Put on your shoes.

If particles are not taken care of, your shoes on put

ninna chappali meile id’u

(meaning: put your shoes on top)

Sandhi(1)

• South Indian languages (Dravidian) are rich in Sandhis

• the tense, gender and number present in a sentence inflect the verbs

English Sentence Kannada Sentence

1. He plays in my house nanna maneyalli avanu aad’uttaane

2. She plays in my house nanna maneyalli aval’u aad’uttaal’e

3. He is playing in my house nanna maneyalli avanu aad’uttiddaane

4. She is playing in my house

nanna maneyalli aval’u aad’uttiddaal’e

Sandhi(2)

word to word translation gave the following output:

nanna mane alli avanu aad’u ide

Actual Kannada translation:

nanna mane alli avanu aad’uttiddaane

To solve this problem, the following is stored in a file,he_is_verb ttiddaaneshe_is_verb ttiddaal’ehe_is_adj yavanushe_is_adj yaval’u*_is_not_verb ttillathey_is_verb ttidaarehe_will_verb ttaaneshe_will_verb ttaal’ethey_will_verb ttaarehe_will_be_verb ttirutaane*independent of gender

Sample:

A part of the [auxiliary/modal]-[verb]-translation list

Sandhi(2)Input: he is playing in my house (a)

nanna maneyalli avanu aad’u ide (b)

The translation of any “auxiliary verb”, “modal verb” and “not” present in the sentence are removed from (b) to get,

nanna maneyalli avanu aad’u (c)

“he_is_playing” matches with the sequence, “he_is_verb” in the list

nanna maneyalli avanu aad’u ttidaane

he_is_verb ttiddaaneshe_is_verb ttiddaal’ehe_is_adj yavanushe_is_adj yaval’u*_is_not_verb ttillathey_is_verb ttidaarehe_will_verb ttaaneshe_will_verb ttaal’ethey_will_verb ttaarehe_will_be_verb ttirutaane*independent of gender

Evaluation

Input sentence: he is going home for lunch

Output sentence from the system: Ootakke avanu mane hooguttidaane (BLEU

(N=3) = 0)(Should have been: Ootakke avanu manege hooguttidaane)

Although the translation implies its meaning, the BLEU score returns a score of 0 (absence of trigrams (N=3)).

Give a fair score

the words with sandhis were split before (human),

reference sentence: Oota kke avanu mane ge hooguttidaane

The output from the system was split as,

candidate sentence: Oota kke avanu mane hooguttidaane

BLEU Score: 0.6237

Results

Where,1 Performance of EBMT system without Rules

2 Performance of EBMT system with 582 Rules

BLEU Score Results for Kannada

With Rules 500

Without Rules 500

BLEU Score Evaluation for Kannada Corpora

Sentences Taken for Evaluation is 100

Sentences Taken for Evaluation is 100With Rules 500

Without Rules 500


Effect of number of rules (from G-EBMT) on accuracy

• substantial improvement in the average score when Module1 (G-EBMT) and Module2 (language specific-linguistic rules) are combined.

• errors mainly due to ambiguities in the meaning of the words

How can we improve further

• Wikipedia approach• Human Evaluation rather than BLEU• Use linguistic expertise to generate more

Linguistic rules• We are also using data mining rules to infer

rules from the corpora

With Rules 500

Without Rules 500Sentences Taken for Evaluation is 100

BLEU Score Evaluation for Tamil Corpora

With Rules 500

Without Rules 500


Sentences Taken for Evaluation is 100

Results

Where, 1 Performance of EBMT system without Rules

2 Performance of EBMT system with 700 rules

BLEU Score results for Tamil

Human Evaluation of Machine Translation

• Because of the problems in word ordering, BLEU gives Because of the problems in word ordering, BLEU gives minimum score. So evaluation is done by human beings in minimum score. So evaluation is done by human beings in a test bed and the results are as follows:a test bed and the results are as follows:

http://ashwini.dli.ernet.in/humanmt/• A sample of this application can be found at the following URL:A sample of this application can be found at the following URL:

Post Editing of Translated sentences

• The EBMT system possesses all the necessary words to yield The EBMT system possesses all the necessary words to yield a perfect translation. But still it is getting only 80% accuracy. a perfect translation. But still it is getting only 80% accuracy.

• What lags here is the choosing of correct word at the correct What lags here is the choosing of correct word at the correct position. This can be done by editing the translated sentence. position. This can be done by editing the translated sentence. For this AJAX (Asynchronous JavaScript And XML) is used. For this AJAX (Asynchronous JavaScript And XML) is used.

• Since, whenever the user wants to change a word, if it was Since, whenever the user wants to change a word, if it was deployed conventionally, then for each word the whole page deployed conventionally, then for each word the whole page has to be refreshed each time results an annoying situation. has to be refreshed each time results an annoying situation.

• For this, AJAX has been deployed since it doesn't need any For this, AJAX has been deployed since it doesn't need any refresh. refresh.

• By this effort the translation accuracy increased By this effort the translation accuracy increased drastically,nearing human quality. A sample of this has been drastically,nearing human quality. A sample of this has been hosted at hosted at http://ashwini.dli.ernet.in/ebmtpe/http://ashwini.dli.ernet.in/ebmtpe/

Wikipedia approach to EBMT

• A community portal has been created and anyone who is willing to A community portal has been created and anyone who is willing to donate a word/phrase/sentence can do it by this approach. They can donate a word/phrase/sentence can do it by this approach. They can evaluate the EBMT translation quality also. evaluate the EBMT translation quality also.

• A moderator will take care of the added/corrected words, phrases A moderator will take care of the added/corrected words, phrases and sentences. He/she will decide whether to omit the entry or add it and sentences. He/she will decide whether to omit the entry or add it to the databaseto the database

• The URLs of the community portal are as follows:-The URLs of the community portal are as follows:-• http://ashwini.dli.ernet.in/community/wordfinder.htmlhttp://ashwini.dli.ernet.in/community/wordfinder.html

• http://ashwini.dli.ernet.in/community/phrasefinder.htmlhttp://ashwini.dli.ernet.in/community/phrasefinder.html

http://ashwini.dli.ernet.in/community/sentencefinder.htmlhttp://ashwini.dli.ernet.in/community/sentencefinder.html

• http://ashwini.dli.ernet.in/mod.phphttp://ashwini.dli.ernet.in/mod.php

Wikipedia approach to EBMTPublic Interface- Flow Chart

START

Main Interface

Enter word Enter phrase Enter sentence

Word Availability Phrase Availability Sentence Availability

Add word

Correct word

Add phrase

Correct phrase

Add sentence

Correct sentence

Word

Wordfound

PhraseNot

found

Phrasefound

sentenceNot found

Sentencefound

WordNot

found

Phrase

Sentence

Wikipedia approach to EBMTAdding a word

Wikipedia approach to EBMTAdding a phrase

Wikipedia approach to EBMTAdding a sentence

Wikipedia approach to EBMTModerator part - Flowdiagram

Wikipedia approach to EBMTModerator Part

Wikipedia approach to EBMTModerator part

English-Hindi EBMT

English-Hindi EBMT: Result

English-Kannada EBMT

English-Kannada EBMT: Result

English-Tamil EBMT

English-Tamil EBMT: Result

Conclusion

• Many problems in Indian languages are looking more and more intractable like the legendary Indian language OCR !

• What is succesful in English and European languages need not be successful for Indian languages

• A whole new way of thinking may be needed-• Overall UDL will turn out to be a very fertile ground

for Research- many unsolved problems for which we do not even know the directions-

• Good Enough Technologies are part of the new way of thinking

The Websites to watchThe Websites to watch

• http://www.new.dli.ernet.in/

• http://www.dli.ernet.in/

• http://dli.iiit.ac.in/

• http://swati.dli.ernet.in/om

• http://bharani.dli.ernet.in/ebmt/

• http://revati.dli.ernet.in/SearchTamil.html

Acknowledgements

• Prof Raj Reddy for his great vision and Guidance

• Madhavi, Hemant, Eric, Krishna, Kiran, Srini, Sravan, Sheik, Mini, Rashmi, Pradeepa, Tina, Malar, Anand, Jiju, Ravi, Vamshi, Vivek,

Vinodini and Kishore

It happens only in

India

the motivation- statements by prof raj reddy information will be read by both humans and machines -...

Documents

tamil slide

beautiful translation

good translation

days slide

usage slide

corresponding translation

semantic error slide

translation constraints