The Motivation- Statements by Prof Raj Reddy

3. 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?

4. The Google • It removes the stop words • It stems • It does not disambiguate • It makes you wonder why you did such a beautiful Translation

5. 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

6. 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

7. 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

8. 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

9. 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.

10. Good Enough Translation

11. 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

12. Problem statement Aim: • To obtain a “good enough” translation Constraints: • Limited Data • Limited Processing Time

13. Corpora Table (Indian Languages)

14. Corpora Languages Corpora Table (Indian Languages)

15. 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 rule Source 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

16. Word Order Free – A feature of Indian languages • 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

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

18. 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.

19. 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.

20. Bhima killed an elephant named‘ Ashwathama '. Then he loudly announced for all to hear, ‘Ashwathama Hathah Kunjarah’ Ashwathama– an elephant, is killed

21. Lord Krishna blows his conch and makes the wordKunjarah (elephant) inaudible in the battlefield.

22. Drona turned to Yudhisthira and asked if that was true. Yudhisthira said, “Ashwathama is killed - An Elephant ” he added in a low voice

23. Dronacharya hears only the first two words ‘Ashwathama Hathah’, Presumed that his son ‘Ashwathama’ has been killed.

24. He gives up his weapons and sits in prayer. Dhristadymna takes advantage of the opportunity and kills Dronacharya.

25. This story depicts Indian Languages likeSanskrit are Word Order Free Languages- Hence good lexical Corpora would help in making nearly good enough Translation

26. 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 theydid not avarumaadalilla follow the<noun> <noun>annu paalisi follow therulesniyamagal’l’annu paalisi Output sentence:avarumaadalillaniyamagal’l’annu paalisi(BLEU score : 0) (Word order is very important) Sentence Level-Generalization: <pron>did not follow the<noun>  <pron> <noun> annu paalisalilla Output sentence:avaru niyamagal’l’annu paalisalilla(BLEU score : 1)

27. 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? What do we do if it does? When will this happen?

28. Proposed Method

29. 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

30. Applying Language Specific Rules

31. Stage1: Tagger and Stemmer (1) Input sentence: he is playing in my house Output of the tagger*: he_PPis_VBZplay_VBGin_INmy_PP$house_NN PP- Personal pronoun VBZ-verb, 3rd person singular present VBG-verb, gerund or present participle IN-Preposition or subordinating conjunction PP$-Possessive pronoun NN-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.

32. Stage1: Retagging (2) Why? Auxiliary verbs are different from other verbs in Indian Languages he_PPis_auxvplay_VBGin_INmy _PP$house_NN

33. Stage2: Splitting based on Prepositions and Conjunctions and Reordering (1) Original sentence: with preposition he is playingin my house P1:in my house P2: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 freeto develop her ideasandto distribute it E1: She is free E2: develop her ideas to E3: and E3: distribute it to

34. Stage2: Splitting based on Prepositions and Conjunctions and Reordering (2) Special case: two or more prepositions I am going to schoolwith Mary After splitting and Reordering: P1: Mary with (Mary jote) P2: school to (shaale ge) P3: I am go (naanu hooguttidene) Mary joteshaalege naanu hooguttidene

35. Stage3: Reorder Interrogations and Verbs Place the verbs at the end: P1:my house in P2: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

36. Stage 4: Reorder Auxiliary/Modal verbs P1: my house in P2: he play is(isis placed at the end of P2) Special Case: He is not playing in my house E1: my house in E2: he play is not

37. 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

38. 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)

39. Sandhi(1) • South Indian languages (Dravidian) are rich in Sandhis • the tense, gender and number present in a sentence inflect the verbs

40. 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, Sample: he_is_verb ttiddaane she_is_verb ttiddaal’e he_is_adj yavanu she_is_adj yaval’u *_is_not_verb ttilla they_is_verb ttidaare he_will_verb ttaane she_will_verb ttaal’e they_will_verb ttaare he_will_be_verb ttirutaane *independent of gender A part of the [auxiliary/modal]-[verb]-translation list

41. 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’uttidaane he_is_verb ttiddaane she_is_verb ttiddaal’e he_is_adj yavanu she_is_adj yaval’u *_is_not_verb ttilla they_is_verb ttidaare he_will_verb ttaane she_will_verb ttaal’e they_will_verb ttaare he_will_be_verb ttirutaane *independent of gender

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

43. 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

44. Results BLEU Score Results for Kannada Where, 1  Performance of EBMT system without Rules 2  Performance of EBMT system with 582 Rules

45. BLEU Score Evaluation for Kannada Corpora With Rules 500 Without Rules 500 Sentences Taken for Evaluation is 100

46. BLEU Score Evaluation for Kannada Corpora With Rules 500 Without Rules 500 Sentences Taken for Evaluation is 100

47. 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

48. 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

49. BLEU Score Evaluation for Tamil Corpora With Rules 500 Without Rules 500 Sentences Taken for Evaluation is 100

50. BLEU Score Evaluation for Tamil Corpora With Rules 500 Without Rules 500 Sentences Taken for Evaluation is 100