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Machine Translation across Indian Languages

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

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Machine Translation across Indian Languages

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  1. Machine Translation across Indian Languages Dipti Misra Sharma LTRC, IIIT Hyderabad Patiala 15-11-2013

  2. 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

  3. 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

  4. Natural Language Processing (NLP) Helps in • Communication between • Man-machine • Question answering systems, eg interactive railway reservation • Man – man • Machine translation

  5. 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)

  6. 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

  7. 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

  8. 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

  9. Information Dynamics in Language (2/4)‏ Ambiguity in Hindi is resolved if, cuuhe maarate haiM kuttoM ko rats kill-hab pres-pl dogs-obl acc • Hindi encodes information in morphemes • English encodes information in positions Languages encode information differently

  10. 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

  11. 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

  12. Information Dynamics in Language (4/4)‏ English pronouns he, she, it Hindi pronoun vaha He is going to Delhi ==> vahadilli jaa rahaahai She is going to Delhi ==> vahadillii jaa rahiihai Itbroke ==> vaha TuuTa ?? Information does notalwaysmap fullyfrom one language into another Conceptual worlds may be different Gender Information

  13. Information in Language • Languages encode information differently • Languages code information onlypartially • Tension between BREVITY and PRECISION

  14. 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 ?

  15. Languages differ • Script (For written language)‏ • Vocabulary • Grammar These differences can be considered as a measure of language distance

  16. Language Distance Script -------------- Vocabulary----------Grammar Urdu-> Hindi Telugu -> Hindi Telugu->Hindi English -> Hindi English-> Hindi English->Hindi

  17. 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

  18. 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 metwho everyday with you cricket plays TL synthesis    : mEM eka laDake se milAjo roza tumhAre sAtha kriketa khelatA hE OR mEMroza tumhAre sAtha kriketa khelanevAle eka laDake se milA OR meMekaEse laDake semilAjo roza tumhAre sAtha kriketa khelatA hE

  19. 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

  20. 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'

  21. Linguistic Issues in MT (2/2)‏ • Ambiguity in Language • Lexical level • Sentence level • Structural differences between SL and TL

  22. 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

  23. 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)‏

  24. 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)‏

  25. Function words can also pose problems (1/5)‏ • Prepositions • English prepositions in the target language ‏ • Tense Aspect Modality (TAM)‏ • Lexical correspondence of TAM

  26. 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 subaha0 milA 'Ambiguity' here refers to the 'appropriate correspondence' in the target language.

  27. 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’ .

  28. 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)‏

  29. 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)‏

  30. 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)‏

  31. Sentence level ambiguity o I met the girl in the store      + Possible readingsa) I met the girl who works in the store b)I met the girl while I was in the storeo 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)

  32. 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'

  33. 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

  34. Human beings use • World Knowledge • Context • Cultural knowledge and • Language conventions To resolve ambiguities and interpret meaning

  35. 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

  36. 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

  37. 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

  38. 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)

  39. 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)

  40. 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)

  41. MT Efforts in India (4/4) Anusaaraka : Language Accesspr cum MT System (IIIT, Hyderabad, Chinmaya Shodh Sansthan)

  42. Our Focus Sampark : Indian Language to Indian Language MT systems <sampark.org.in>

  43. Sampark : Indian Language to Indian Language MT Systems • Consortium mode project • Funded by DeiTY • 11 Partiicpating Institutes • Nine language pairs • 18 Systems

  44. 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

  45. 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.

  46. Language Pairs (Bidirectional) • Tamil-Hindi • Telugu-Hindi • Marathi-Hindi • Bengali-Hindi • Tamil-Telugu • Urdu-Hindi • Kannada-Hindi • Punjabi-Hindi • Malayalam-Tamil

  47. 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

  48. 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

  49. 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

  50. 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

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