1. Machine Translation Om Damani (Ack: Material taken from JurafskyMartin 2 nd Ed., Brown et. al. 1993)

2. 2 The spirit is willing but the flesh is weak English-Russian Translation System Дух охотно готов но плоть слаба Russian-English Translation System The vodka is good, but the meat is rotten State of the Art Babelfish: Spirit is willingly ready but flesh it is weak Google: The spirit is willing but the flesh is week

3. 3 The spirit is willing but the flesh is weak Google English-Hindi Translation System आत्मा पर शरीर दुर्बल है Google Hindi-English Translation System Spirit on the flesh is weak State of the Art (English-Hindi) – March 19, 2009

4. 4 Is state of the art so bad Google English-Hindi Translation System कला की हालत इतनी खराब है Google Hindi-English Translation System The state of the art is so bad Is State of the Art (English-Hindi) so bad

5. 5 State of the english hindi translation is not so bad Google English-Hindi Translation System राज्य के अंग्रेज़ी हिन्दी अनुवाद का इतना बुरा नहीं है Google Hindi-English Translation System State of the English translation of English is not so bad State of the english-hindi translation is not so bad OK. Maybe it is __ bad.

6. 6 State of the English Hindi translation is not so bad Google English-Hindi Translation System राज्य में अंग्रेजी से हिंदी अनुवाद का इतना बुरा नहीं है Google Hindi-English Translation System English to Hindi translation in the state is not so bad State of the English-Hindi translation is not so bad OK. Maybe it is __ __ bad. राज्य के अंग्रेज़ी हिन्दी अनुवाद का इतना बुरा नहीं है

7. 7 Your Approach to Machine Translation

8. 8 Translation Approaches

9. 9 Direct Transfer – What Novices do

10. 10 Direct Transfer: Limitations Lexical Transfer: Many Bengali poet-PL,OBL this land of songs {sing has}- PrPer,Pl Many Bengali poets have sung songs of this land Final: Many Bengali poets of this land songs have sung Local Reordering: Many Bengali poet-PL,OBL of this land songs {has sing}- PrPer,Pl कई बंगाली कवियों ने इस भूमि के गीत गाए हैं Kai Bangali kaviyon ne is bhoomi ke geet gaaye hain Morph: कई बंगाली कवि -PL,OBL ने इस भूमि के गीत { गाए है }-PrPer,Pl Kai Bangali kavi-PL,OBL ne is bhoomi ke geet {gaaye hai}-PrPer,Pl

11. 11 Syntax Transfer (Analysis-Transfer-Generation) Here phrases NP, VP etc. can be arbitrarily large

12. 12 Syntax Transfer Limitations He went to Patna -> Vah Patna gaya He went to Patil -> Vah Patil ke pas gaya Translation of went depends on the semantics of the object of went Fatima eats salad with spoon – what happens if you change spoon Semantic properties need to be included in transfer rules – Semantic Transfer

13. 13 Interlingua Based Transfer you this farmer agtobj pur plc contact nam or region khatav manchar taluka nam :01 For this, you contact the farmers of Manchar region or of Khatav taluka. In theory: N analysis and N transfer modules in stead of N 2 In practice: Amazingly complex system to tackle N 2 language pairs

14. 14 Difficulties in Translation – Language Divergence ( Concepts from Dorr 1993, Text/Figures from Dave, Parikh and Bhattacharyya 2002 ) Constituent OrderPrepositional StrandingNull Subject Conflational Divergence Categorical Divergence

15. 15 Lost in Translation: We are talking mostly about syntax, not semantics, or pragmatics You: Could you give me a glass of water Robot: Yes. ….wait..wait..nothing happens..wait… …Aha, I see… You: Will you give me a glass of water …wait…wait..wait.. Image from http://inicia.es/de/rogeribars/blog/lost_in_translation.gif

16. 16 CheckPoint  State of the Art  Different Approaches  Translation Difficulty  Need for a novel approach

17. 17 Statistical Machine Translation: Most ridiculous idea ever Consider all possible partitions of a sentence. For a given partition, Consider all possible translations of each part. Consider all possible combinations of all possible translations Consider all possible permutations of each combination And somehow select the best partition/translation/permutation कई बंगाली कवियों ने इस भूमि के गीत गाए हैं Kai Bangali kaviyon ne is bhoomi ke geet gaaye hain कई बंगाली कवियोंने इसभूमि केगीत गाए हैं Many Bengali Poetsthislandofhave sung poem Several Bengalito thisplace‘ssing songs Many poets from Bangal in thisspacesong sung Poets from Bangladesh farmhave sung songs To this space have sung songs of many poets from Bangal

18. 18 How many combinations are we talking about Number of choices for a N word sentence N=20 ?? Number of possible chess games

19. 19 How do we get the Phrase Table Collect large amount of bi-lingual parallel text. For each sentence pair, Consider all possible partitions of both sentences For a given partition pair, Consider all possible mapping between parts (phrases) on two side Somehow assign the probability to each phrase pair इसके लिए आप मंचर क्षेत्र के किसानों सॆ संपर्क कीजिए For this you contact the farmers of Manchar region

20. 20 Data Sparsity Problems in Creating Phrase Table Sunil is eating mangoe -> Sunil aam khata hai Noori is eating banana -> Noori kela khati hai Sunil is eating banana -> We need examples of everyone eating everything !! We want to figure out that eating can be either khata hai or khati hai And let Language Model select from ‘Sunil kela khata hai’ and ‘Sunil kela khati hai’ Select well-formed sentences among all candidates using LM

21. 21 Formulating the Problem. A language model to compute P(E). A translation model to compute P(F|E). A decoder, which is given F and produces the most probable E

22. 22 P(F|E) vs. P(E|F) P(F|E) is the translation probability – we need to look at the generation process by which pair is obtained. Parts of F correspond to parts of E. With suitable independence assumptions, P(F|E) measures whether all parts of E are covered by F. E can be quite ill-formed. It is OK if {P(F|E) for an ill-formed E} is greater than the {P(F|E) for a well formed E}. Multiplication by P(E) should hopefully take care of it. We do not have that luxury in estimating P(E|F) directly – we will need to ensure that well-formed E score higher. Summary: For computing P(F|E), we may make several independence assumptions that are not valid. P(E) compensated for that. P( बारिश हो रही है |It is raining) =.02 P( बरसात आ रही है | It is raining) =.03 P( बारिश हो रही है |rain is happening) =.420 We need to estimate P(It is raining| बारिश हो रही है ) vs. P(rain is happening| बारिश हो रही है )

23. 23 CheckPoint  From a parallel corpus, generate probabilistic phrase table  Give a sentence, generate various candidate translations using the phrase table  Evaluate the candidates using Translation and Language Models

24. 24 What is the meaning of Probability of Translation  What is the meaning of P(F|E)  By Magic: you simply know P(F|E) for every (E,F) pair – counting in a parallel corpora  Or, each word in E generates one word of F, independent of every other word in E or F  Or, we need a ‘random process’ to generate F from E  A semantic graph G is generated from E and F is generated from G We are no better off. We now have to estimate P(G|E) and P(F|G) for various G and then combine them – How? We may have a deterministic procedure to convert E to G, in which case we still need to estimate P(F|G)  A parse tree T E is generated from E; T E is transformed to T F; finally T F is converted into F Can you write the mathematical expression

25. 25 The Generation Process  Partition: Think of all possible partitions of the source language  Lexicalization: For a give partition, translate each phrase into the foreign language  Spurious insertion: add foreign words that are not attributable to any source phrase  Reordering: permute the set of all foreign words - words possibly moving across phrase boundaries Try writing the probability expression for the generation process We need the notion of alignment

26. 26 Generation Example: Alignment

27. 27 Simplify Generation: Only 1->Many Alignments allowed

28. 28 Alignment A function from target position to source position: The alignment sequence is: 2,3,4,5,6,6,6 Alignment function A: A(1) = 2, A(2) = 3.. A different alignment function will give the sequence:1,2,1,2,3,4,3,4 for A(1), A(2).. To allow spurious insertion, allow alignment with word 0 (NULL) No. of possible alignments: (I+1) J

29. 29 CheckPoint  From a parallel corpus, generate probabilistic phrase table  Give a sentence, generate various candidate translations using the phrase table  Evaluate the candidates using Translation and Language Models  Understanding of Generation Process is critical  Notion of Alignment is important