Machine Translations Across Indian Languages-I, II

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Machine Translation across
Indian Languages
Dipti Misra Sharma
LTRC, IIIT
Hyderabad
Patiala
15-11-2013
Outline
• Introduction
• Information Dynamics in language
• Machine Translation (MT)
• Approaches to MT
• Practical MT systems
• Challenges in MT
• Ambiguities
• Syntactic differences in L1 an L2
• MT efforts in India
–Sampark : IL to IL MT systems
– Objective
– Design
– Issues
• Conclusions
Introduction
Natural Language Processing (NLP) involves
 Processing information contained in natural
languages
 Natural as opposed to formal/artificial
 Formal languages : Programming languages, logic,
mathematics etc
 Artificial : Esperanto
Natural Language Processing (NLP)
Helps in
 Communication between
 Man-machine
 Question answering systems,
eg interactive railway reservation
 Man – man
 Machine translation
Communication
Transfer of information from one to the other
Language is a means of communication
Therefore, one can say
It encodes what is communicated <information>
We apply the processes of
Analysis (decoding) for understanding
Synthesis (encoding) for expression (speaking)
What do we communicate ?
Information
Spain delivered a football masterclass at Euro 2012
Intention <purpose>

Emphasis/focus
 Euro 2012 bagged/won by Spain
 Spain bags Euro 2012
• Introduces variation
How do we communicate ? Contd..
Arrangement of sentences (Discourse)
Sentences or parts of sentences are related to each other to
provide a cohesive meaning
*Considered as one of the best wild life sanctuaries in the
country. It is a national park covering an area of about 874 km.
Bandipur National park is a beautiful tourist spot.
Bandipur National park is a beautiful tourist spot and
considered as one of the best wild life sanctuaries in the
country. It is a national park covering an area of about 874 km
Languages differ in the way they organise information in
these entities
All of these interact in the organisation of information
Information Dynamics in Language (1/4)
• Languages encode information
Hindi: cuuhe maarate haiM

kutte
'rat-pl' 'kill-hab' 'pres-pl' 'dog-pl'
rats
kill
dogs
 Hindi sentence is ambiguous
 Possible interpretations
Dogs kill rats
Rats kill dogs
However,
English sentence is not ambiguous
Information Dynamics in Language (2/4)
Ambiguity in Hindi is resolved if,
cuuhe maarate haiM
rats
kill-hab pres-pl
kuttoM ko
dogs-obl acc
 Hindi encodes information in morphemes
 English encodes information in positions
Languages encode information differently
• English does not explicitly mark accusative case
(except in pronouns) – no morpheme
• No lexical item/morpheme for yes no questions
(Eng: Is he coming ? Hindi : kyaa vah aa rahaa hai?)
• Position plays an important role in encoding
information in English
• Subject is sacrosanct
• Hindi encodes information morphologically
Information Dynamics in Language (3/4)
Another example,
This chair has been sat on
– The chair has been used for sitting
– Someone sat on this chair, and it is known
– The sentence does not mention someone
Languages encode information partially
Information Dynamics in Language (4/4)
English pronouns
Hindi pronoun
he, she, it
vaha
Gender Information
He is going to Delhi ==>
She is going to Delhi ==>
It broke
==>
vaha dilli jaa rahaa hai
vaha dillii jaa rahii hai
vaha TuuTa ??
Information does not always map fully from one language into
another
Conceptual worlds may be different
Information in Language
• Languages encode information differently
• Languages code information only partially
• Tension between BREVITY and PRECISION
Human beings use
 World knowledge
 Context (both linguistic and extra-linguistic)
 Cultural knowledge and
 Language conventions
to resolve ambiguities
Can all this knowledge be provided to the
machine ?
Languages differ
• Script (For written language)
• Vocabulary
• Grammar
These differences can be considered as a measure
of language distance
Language Distance
Script -------------- Vocabulary----------Grammar
Urdu-> Hindi
Telugu -> Hindi
English -> Hindi
Telugu->Hindi
English-> Hindi
English->Hindi
Machine Translatoion
Machine translation aims at automatic translation of
a text in source language to a text in the target
language.
Mohan gave Hari a book -> Mohan ne Hari ko kitAba dI
English to Hindi : An Example
SL (Eng) sentence : I met a boy who plays cricket with you
everyday
Mapped to TL(Hin) : I a boy met who everyday with you cricket
plays
TL synthesis : mEM eka laDake se milA jo roza tumhAre sAtha
kriketa khelatA hE
OR
mEM roza tumhAre sAtha kriketa khelanevAle eka
laDake se milA
OR
meM eka Ese laDake se milA jo roza tumhAre sAtha
kriketa khelatA hE
Machine Translation : Challenges
• Languages encode information differently
• Language codes information only partially
• Tension between BREVITY and PRECISION
• Brevity wins leading to inherent ambiguity at different levels
Linguistic Issues in MT (1/2)
Look at the word 'plot' in the following examples
(a) The plot having rocks and boulders is not good.
(b) The plot having twists and turns is interesting.
'plot' in (a) means 'a piece of land' and
in (b) 'an outline of the events in a story'
Linguistic Issues in MT (2/2)
 Ambiguity in Language
•
Lexical level
 Sentence level
 Structural differences between SL and TL
Lexical ambiguity
Lexical ambiguity can be both for
Content words – nouns, verbs etc
Function words – prepositions, TAMs etc
 Content words ambiguity is of two types
Homonymy
Polysemy
Homonymy
A word has two or more unrelated senses
Example :
I was walking on the bank (river-bank)
I deposited the money in the bank (money-bank)
Polsysemy
'Act', an English noun
1. It was a kind act to help the blind man across the
road (kArya)
2. The hero died in the Act four, scene three (aMka)
3. Don't take her seriously, its all an act (aBinaya)
4. The parliament has passed an Act (dhArA)
Function words can also pose
problems (1/5)
 Prepositions
 English prepositions in the target language
 Tense Aspect Modality (TAM)
 Lexical correspondence of TAM
Function words can also pose problems
(2/5)
Function words can also be ambiguous
For example – English preposition 'in'
(a) I met him in the garden
mEM usase bagIce meM milA
(b) I met him in the morning
mEM usase subaha 0 milA
'Ambiguity' here refers to the 'appropriate correspondence' in the
target language.
Function words can also pose problems(3/5)
He bought a shirt with tiny collars.
usane chote kOlaroM vAlI kamIza kharIdI
‘he
tiny
collars
with
shirt
bought’
‘with’ gets translated as ‘vAlI’ in hindi
He washed a shirt with soap.
usane sAbuna se kamIza dhoI
‘he
soap
with shirt
washed’
‘with’ gets translated as ‘se’ .
Function words can also pose problems
(4/5)
TAM Markers mark tense, aspect and modality
Consist of inflections and/or auxiliary verbs
in Hindi
An important source of information
Narrow down the meaning of a verb (eg.
lied, lay)
Function words can also pose problems
(4/5)
TAM Markers mark tense, aspect and modality
Consist of inflections and/or auxiliary verbs
in Hindi
An important source of information
Narrow down the meaning of a verb (eg.
lied, lay)
Function words can also pose problems
(5/5)
English Simple Past vs Habitual'
1a. He stayed in the guest house during his visit to our University in
Jan (rahA)
1b. He stayed in the guest house whenever he visited us (rahatA
thA)
2a. He went to the school just now (gayA)
2b. He went to the school everyday (jAtA thA)
Sentence level ambiguity
o I met the girl in the store
+ Possible readings
a) I met the girl who works in the store
b) I met the girl while I was in the store
o Time flies like an arrow.
+ Possible parses:
a) Time flies like an arrow (N V Prep Det N)
b) Time flies like an arrow (N N V Det N)
c) Time flies like an arrow (V N Prep Det N) (flies are like an
arrow)
d) Time flies like an arrow (V N Prep Det N) (manner of
timing)
Differences in SL and TL
Lexical level
(a) One word may translate into different words in different
contexts (WSD)
English 'plot' → zamiin, kathanak
(b) A SL word may not have a corresponding word in the
TL (Gaps)
English 'reads' in 'This book reads very well'
(d) Pronouns across Indian languages
Hindi 'vaha' → Telugu 'adi', 'atanu', 'aame'
Differences in SL and TL
Structural differences
(a) word order (English – Hindi)
(b) nominal modification (Hindi – Tamil, Telugu
etc)
(i) relative clause vs relative participles
Telugu 'nenu tinnina camcaa'
Hindi : *meraa khaayaa cammaca
Maine jis cammaca se khaayaa hai vah
cammac
(ii) missing copula (Hindi – Telugu, Bengali, Tamil
etc)
Telugu : raamudu mancivaadu
Hindi : Ram acchaa ladakaa hai
Human beings use
World Knowledge
Context
Cultural knowledge and
Language conventions
To resolve ambiguities and interpret meaning
What to do for the machine ?
Challenging problem!!!
 Providing all the knowledge may:
- take too much of time and effort
- be difficult/become complex
- not be possible (world knowledge acquired from
experience)


Therefore,
 Break the problem into smaller problems
 Choose the solution as per the nature of
problem
 Build language resources to the extent possible
and continue to add to it
Engineer knowledge efficiently
Approaches to MT (1/2)
 Rule-based or Transfer based
 Uses linguistic rules to map SL and TL, such as
• Maps grammatical structures
• Disambiguation rules
• Knowledge-based
• Extensive knowledge of the domain
• Concepts in the language
• Ability to reason
Approaches to MT (2/2)
• Example-based
• Mapping is based on stored example translations
•
Translation memory based
• Uses phrases/words from earlier translation as
examples
 Statistical
Does not formulate explicit linguistic knowledge
Develops rules based on probabilities
 Hybrid
Mixes two or more techniques
A Glance at MT Efforts in
India (1/4)
 Domain Specific
 Mantra system (C-DAC, Pune)
 Translation of govt. appointment letters
 Uses Tree Adjoining Grammar
 Public health compaign documents
Angla Bharati approach (C-DAC Noida & IIT Kanpur)
A Glance at MT Efforts in
India (2/4)
 Application Specific
 Matra (Human aided MT) (NCST,now C-DAC, Mumbai)
 General Purpose (not yet in use)
 Angla Bharati approach (IIT Kanpur )
 UNL based MT (IIT Bombay)
 Shiva: EBMT (IIIT Hyderabad/IISc Bangalore)
 Shakti: English-Hindi MT system (IIIT Hyderabad)
MT Efforts in India (3/4)
Major Government funded MT projects in consortium mode
Indian Language to Indian Language Machine Translation
(ILMT) (Lead Institute - IIIT, Hyderabad)

English to Indian Language Machine Translation



Mantra, Shakti etc (Lead inst - C-DAC, Pune)

Anglabharati (Lead inst – IIT, Kanpur)
Sanskrit to Hindi MT System (Lead Inst – University of
Hyderabad)
MT Efforts in India (4/4)
Anusaaraka : Language Accesspr cum MT System
(IIIT, Hyderabad, Chinmaya Shodh Sansthan)
Our Focus
Sampark : Indian Language to Indian Language
MT systems
<sampark.org.in>
Sampark : Indian Language to
Indian Language MT Systems
• Consortium mode project
• Funded by DeiTY
• 11 Partiicpating Institutes
• Nine language pairs
• 18 Systems
Participating institutions
 IIIT,
Hyderabad (Lead institute)
 University of Hyderabad
 IIT, Bombay
 IIT, Kharagpur
 AUKBC, Chennai
 Jadavpur University, Kolkata
 Tamil University, Thanjavur
 IIIT, Trivandrum
 IIIT, Allahabad
 IISc, Bangalore
 CDAC, Noida
Objectives


Develop general purpose MT systems from one IL to another

for 9 language pairs

Bidirectional
Deliver domain specific versions of the MT systems. Domains are:



Tourism and pilgrimage
One additional domain (health/agriculture, box office reviews, electronic
gadgets instruction manuals, recipes, cricket reports)
By-products basic tools and lexical resources for Indian languages:


POS taggers, chunkers, morph analysers, shallow parsers, NERs, parsers
etc.
Bidirectional bilingual dictionaries, annotated corpora, etc.
Language Pairs (Bidirectional)
 Tamil-Hindi
 Telugu-Hindi
 Marathi-Hindi
 Bengali-Hindi
 Tamil-Telugu
 Urdu-Hindi
 Kannada-Hindi
 Punjabi-Hindi
 Malayalam-Tamil
User Scenario
Web based system for tourism/ pilgrimage domain.
•
•
•
A common traveler/tourist/piligrim to access info in his
language.
Access to selected Government portals in
agriculture/health
•
•
Automatic MT in domain
General purpose web based translation
•
Potential to attach to major search engines such as Google,
Yahoo, Microsoft, Web-duniya
Design and Approach



Largely transfer based
– Analysis, Transfer, Generate
Modular (module could be
Pipeline architecture




Hybrid – some modules statistical, some rule
based
Analysis : Shallow parser
No deep parsing in the first phase
Approach


Largely transfer based
– Analysis, Transfer, Generate
Modular
– Modules could be statistical or rule based depending on
the nature of problem (Hybrid)


Pipeline architecture
Analysis : Shallow parsing followed by a simple
parser
Design
o
Design decisions based on
- the commonality in Indian languages
- easy to extend to other languages
o
Phase the development
- Phase 1
o Analysis at sentence level
o Shallow parser
o Simple parser
o Transfer : map lexicon, structures, script
o Generate the target
Design Contd
Phase 2

Extend the analysis to discourse level

Anaphora resolution

Relations between clauses (discourse
connectives)

Word Sense Disambiguation (WSD)

Named Entity Recognition (NER)

Multi Word Expressions (MWE)

Explore SMT for transfer rules
Transfer based MT
Target Sentence
Source Sentence
Analysis
Source Analysis
Generation
Transfer
Analysis in Target
Language
L1
L1
Form
Form
(Input sentence/text)
Analysis
Generation
Meaning
Various types of linguistic information helps in arriving from form to meaning
It is complex.
Modularization helps in simplifying it.
Modularize
Structure
Word
In context
Morph Analyser
Syntactic (POS tagger)
What is functions as
Semantic
(WSD)
What it means
Relations between words
Local (local word grouping,/ chunking)
Non-local (Subject,object/karaka)
Form
Form
(Input sentence/text)
Morph Analysis
POS
Chunking
Analysis
Generation
parsing
Semantic analysis
Formal semantics
Meaning
All this information is implicit in language.
How to make it explicit?
Build resources – Dictionaries, Verb
frames, Treebanks
Sampark Architecture
Details


Standards

Annotation standards – POS and Chunk

Input – output of each module

Representation - SSF

Data format – Dictionaries
Emphasis on proper software engineering

Development environment – Dashboard



Blackboard architecture
CVS for version control
etc.
Machine Learning: Separating engines
from language data
Module for Task (T)
Training data
(lang. L)
Sentence in Language (L)
Engine for task T
Manual
Correction
Out
Horizontal Tasks
 H1
POS Tagging & Chunking engine
 H2 Morph analyser engine
 H3 Generator engine
 H4 Lexical disambiguation engine
 H5 Named entity engine
 H6 Dictionary standards
 H7 Corpora annotation standards
 H8 Evaluation of output (comprehensibility)
 H9 Testing & integration
Vertical Tasks for Each Language
 V1
POS tagger & chunker
 V2 Morph analyzer
 V3 Generator
 V4 Named entity recognizer
 V5 Bilingual dictionary – bidirectional
 V6 Transfer grammar
 V7 Annotated corpus
 V8 Evaluation
 V9 Co-ordination
Vertical Tasks for Each Language
 V1
POS tagger & chunker
 V2 Morph analyzer
 V3 Generator
 V4 Named entity recognizer
 V5 Bilingual dictionary – bidirectional
 V6 Transfer grammar
 V7 Annotated corpus
 V8 Evaluation
 V9 Co-ordination
An Example : Hindi to Panjabi System
ਭਾਰਤ ਵ ਿੱ ਚ ਆਰੀਆਂ ਦਾ ਆਗਮਨ ਈਸਾ ਦਾ ਕੋਈ 1500 ਸਾਲ ਪੂਰ
ਹੋਇਆ .
ਆਰੀਆਂ ਦਾ ਪਹਲੀ ਖੇਪ ਵਰਗ ੈਵਦਕ ਆਰੀਆ ਕਹਾ ਹੈਂ .
ਵਰਗ ੇਦ ਦਾ ਰਚਨਾ ਇਹ ਸਮਾਂ ਹੋਈ .
ਵਰਗ ੇਦ ਦਾ ਕਈ ਬਾਤੇ ਅ ੇਸਤਾ ਨਾਲ ਵਮਲਦੀ ਹਨ .
ਅ ੇਸਤਾ ਈਰਾਨੀ ਭਾਸ਼ਾ ਦਾ ਪਰਾਚੀਨਤਮ ਗਰੰ ਥ ਹੈਂ .
भारत में आर्यों का आगमन ईसा के कोई 1500 वर्ष पव
ू ष हुआ ।
आर्यों की पहली खेप ऋग्वैदिक आर्यष कहलाती है ।
ऋग्वेि की रचना इसी समर्य हुई ।
ऋग्वेि की कई बाते अवेस्ता से ममलती हैं ।
अवेस्ता ईरानी भार्ा के प्राचीनतम ग्रंथ है ।
Panjabi to Hindi
सरिार उपासक मसंह भारत का एक प्रमख
ु स्वतंत्रता संगराममर्या था .
अमर बबंब बन जाने की कला में उन की कोई सानी नहीं .
उन ने केंद्रीर्य असंबली की बैठक में बम फेंक कर भी भागने से अस्वीकार
कर दिर्या था .
उपासक मसंह को 23 माचष 1931 को उन के साथथर्यों , राजगरू
ु और सख
ु िे व
का से फांसी और लटका दिर्या गर्या था .
संपर्
ू ष िे श ने उन की शहाित को र्याि ककर्या .
ਸਰਦਾਰ ਭਗਤ ਵਸੰ ਘ ਭਾਰਤ ਦੇ ਇਿੱ ਕ ਪਰਮਿੱ ਖ ਅਜਾਦੀ ਸੰ ਗਰਾਮੀਏ ਸਨ।
ਅਮਰ ਵਬੰ ਬ ਬਣ ਜਾਣ ਦੀ ਕਲਾ ਵ ਿੱ ਚ ਉਨਹਾਂ ਦਾ ਕੋਈ ਸਾਨੀ ਨਹੀਂ।
ਉਨਹਾਂ ਨੇ ਕੇਂਦਰੀ ਅਸੰ ਬਲੀ ਦੀ ਬੈਠਕ ਵ ਿੱ ਚ ਬੰ ਬ ਸਿੱ ਟ ਕੇ ੀ ਭਿੱ ਜਣ ਤੋਂ ਇਨਕਾਰ ਕਰ ਵਦਿੱ ਤਾ
ਸੀ।
ਭਗਤ ਵਸੰ ਘ ਨੂੰ 23 ਮਾਰਚ 1931 ਨੂੰ ਉਨਹਾਂ ਦੇ ਸਾਥੀਆਂ, ਰਾਜਗਰੂ ਅਤੇ ਸਖਦੇ ਦੇ ਨਾਲ
ਫਾਂਸੀ ਤੇ ਲਟਕਾ ਵਦਿੱ ਤਾ ਵਗਆ ਸੀ।
ਸਾਰੇ ਦੇਸ਼ ਨੇ ਉਨਹਾਂ ਦੀ ਸ਼ਹਾਦਤ ਨੂੰ ਯਾਦ ਕੀਤਾ।
Panjabi to Hindi
सरिार उपासक मसंह (NER) भारत का एक प्रमख
ु स्वतंत्रता संगराममर्या था .
अमर बबंब (WSD) बन जाने की कला में उन की कोई सानी (Agreement)
नहीं .
उन ने (word generation) केंद्रीर्य असंबली की बैठक में बम फेंक कर भी
भागने से अस्वीकार कर दिर्या था .
उपासक मसंह को 23 माचष 1931 को उन के साथथर्यों , राजगरू
ु और सख
ु िे व
का से (function word substitution) फांसी और लटका दिर्या गर्या था .
संपर्
ू ष िे श ने उन की शहाित को र्याि ककर्या .
Evaluation
Testing, system integration, and evaluation team –
Involvement of industry
• Regular In-house subjective evaluation
• Third party evaluation on system submission
Achievements of ILMT Project Phase I
 18 MT systems built among Indian languages
 Shallow parser for all 9 Indian languages
 Lexical resources for all 9 languages
Largely built from scratch
Developed standards for all stages
Developed open architecture
Achievements -Deployment
Deployed and running over web – 8 systems
(sampark.org.in)
Others deployed over ILMT test site
 4 more ready to go to Sampark soon
 Rest are being evaluated and tested internally
(require a few more months to go to Sampark site after reaching quality
levels)
 Constant qualilty improvement going on for various existing modules
 New modules are under testing and would be soon integrated
Future Tasks
 Enhance the quality of MT output
Enhancing dictionaries
Increasing coverage of grammar
Adding new technology to ILMT systems
Full sentence parsing
Discourse processing - anaphora
Target some users
Some Possibilities
 Possible tie up with search engines companies
 Possible tie up with content companies such as - Dainik
Jagran, Web duniya, Rediff, Yahoo
 Identify translation bureaus and agencies
 Build MT workbench for their use, their domains, etc.
 Poised for major public impact with a unique technology.
Future Systems






Add language pairs
Gujrati – Hindi
Kashmiri – Hindi
Manipuri – Hindi
Oriya – Hindi
Etc
Future Systems






Add language pairs
Gujrati – Hindi
Kashmiri – Hindi
Manipuri – Hindi
Oriya – Hindi
Etc
CONCLUSION
Developing MT systems, though a challenging task,
is a useful effort particularly in the multilingual
context of India
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