1. CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 18– Alignment in SMT and Tutorial on Giza++ and Moses) Pushpak Bhattacharyya CSE Dept., IIT Bombay 15 th Feb, 2011

2. Going forward from word alignment Word alignment Phrase AlignmentDecoding (going to bigger units(best possible Of correspondence) translation)

3. Abstract Problem Given: e o e 1 e 2 e 3 ….e n e n+1 (Entities) Goal: l o l 1 l 2 l 3 ….l n l n+1 (Labels) The Goal is to find the best possible label sequence Generative Model

4. Simplification Using Markov Assumption, the Language Model can be represented using bigrams Similarly translation model can also be represented in the following way:

5. Statistical Machine Translation Finding the best possible English sentence given the foreign sentence P(E)= Language Model P(F|E) = Translation Model E: English, F: Foreign Language

6. Problems in the framework Labels are words of the target language Very large in number Who do you want to_go with ? With whom do you want to go ? _ _ (Aap kis ke_sath jaana chahate_ho)who do doand so onyou want to_go to_gowith Each word have multiple translation options. Preposition Stranding

7. Column of words of target language on the source language words ^ Aap kis ke_sath jaana chahate_ho. whowho do doand so on youyou ^ want want…. to_go to_go withwith Find the best possible path from ^ to. using transition and Observation probabilities. Viterbi can be used

8. TUTORIAL ON Giza++ and Moses tools (delivered by Kushal Ladha)

9. Word-based alignment For each word in source language, align words from target language that this word possibly produces Based on IBM models 1-5 Model 1 – simplest As we go from models 1 to 5, models get more complex but more realistic This is all that Giza++ does

10. Alignment A function from target position to source position: 10 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

11. IBM Model 1: Generative Process 11

12. Training Alignment Models 12 Given a parallel corpora, for each (F,E) learn the best alignment A and the component probabilities: t(f|e) for Model 1 lexicon probability P(f|e) and alignment probability P(a i |a i-1,I) How to compute these probabilities if all you have is a parallel corpora

13. Intuition : Interdependence of Probabilities 13 If you knew which words are probable translation of each other then you can guess which alignment is probable and which one is improbable If you were given alignments with probabilities then you can compute translation probabilities Looks like a chicken and egg problem EM algorithm comes to the rescue

14. Limitation: Only 1->Many Alignments allowed 14

15. Phrase-based alignment More natural Many-to-one mappings allowed

16. Giza++ and Moses Package http://cl.naist.jp/~eric-n/ubuntu-nlp/ Select your Ubuntu version Browse the nlp folder Download debian package of giza++, moses, mkcls, srilm Resolve all the dependencies and they get installed For alternate installation, refer to http://www.statmt.org/moses_steps.html http://www.statmt.org/moses_steps.html

17. Steps Input - sentence aligned parallel corpus Output- target side tagged data Training Tuning Generate output on test corpus (decoding)

18. Training Create a folder named corpus containing test, train and tuning file Giza++ is used to generate alignment Phrase table is generated after training Before training language model needs to be build on target side mkdir lm ; /usr/bin/ngram-count -order 3 -interpolate -kndiscount -text $PWD/corpus/train_surface.hi -lm lm/train.lm; /usr/share/moses/scripts/training/train-factored-phrase-model.perl -scripts- root-dir /usr/share/moses/scripts -root-dir. -corpus train.clean -e hi -f en - lm 0:3:$PWD/lm/train.lm:0;

19. Example train.en h e l l o w o r l d c o m p o u n d w o r d h y p h e n a t e d o n e b o o m k w e e z l e b o t t e r train.pr hh eh l ow hh ah l ow w er l d k aa m p aw n d w er d hh ay f ah n ey t ih d ow eh n iy b uw m k w iy z l ah b aa t ah r

20. Sample from Phrase-table b o ||| b aa ||| (0) (1) ||| (0) (1) ||| 1 0.666667 1 0.181818 2.718 b ||| b ||| (0) ||| (0) ||| 1 1 1 1 2.718 c o m p o ||| aa m p ||| (2) (0,1) (1) (0) (1) ||| (1,3) (1,2,4) (0) ||| 1 0.0486111 1 0.154959 2.718 c ||| p ||| (0) ||| (0) ||| 1 1 1 1 2.718 d w ||| d w ||| (0) (1) ||| (0) (1) ||| 1 0.75 1 1 2.718 d ||| d ||| (0) ||| (0) ||| 1 1 1 1 2.718 e b ||| ah b ||| (0) (1) ||| (0) (1) ||| 1 1 1 0.6 2.718 e l l ||| ah l ||| (0) (1) (1) ||| (0) (1,2) ||| 1 1 0.5 0.5 2.718 e l l ||| eh l ||| (0) (0) (1) ||| (0,1) (2) ||| 1 0.111111 0.5 0.111111 2.718 e l ||| eh ||| (0) (0) ||| (0,1) ||| 1 0.111111 1 0.133333 2.718 e ||| ah ||| (0) ||| (0) ||| 1 1 0.666667 0.6 2.718 h e ||| hh ah ||| (0) (1) ||| (0) (1) ||| 1 1 1 0.6 2.718 h ||| hh ||| (0) ||| (0) ||| 1 1 1 1 2.718 l e b ||| l ah b ||| (0) (1) (2) ||| (0) (1) (2) ||| 1 1 1 0.5 2.718 l e ||| l ah ||| (0) (1) ||| (0) (1) ||| 1 1 1 0.5 2.718 l l o ||| l ow ||| (0) (0) (1) ||| (0,1) (2) ||| 0.5 1 1 0.227273 2.718 l l ||| l ||| (0) (0) ||| (0,1) ||| 0.25 1 1 0.833333 2.718 l o ||| l ow ||| (0) (1) ||| (0) (1) ||| 0.5 1 1 0.227273 2.718 l ||| l ||| (0) ||| (0) ||| 0.75 1 1 0.833333 2.718 m ||| m ||| (0) ||| (0) ||| 1 0.5 1 1 2.718 n d ||| n d ||| (0) (1) ||| (0) (1) ||| 1 1 1 1 2.718 n e ||| eh n iy ||| (1) (2) ||| () (0) (1) ||| 1 1 0.5 0.3 2.718 n e ||| n iy ||| (0) (1) ||| (0) (1) ||| 1 1 0.5 0.3 2.718 n ||| eh n ||| (1) ||| () (0) ||| 1 1 0.25 1 2.718 o o m ||| uw m ||| (0) (0) (1) ||| (0,1) (2) ||| 1 0.5 1 0.181818 2.718 o o ||| uw ||| (0) (0) ||| (0,1) ||| 1 1 1 0.181818 2.718 o ||| aa ||| (0) ||| (0) ||| 1 0.666667 0.2 0.181818 2.718 o ||| ow eh ||| (0) ||| (0) () ||| 1 1 0.2 0.272727 2.718 o ||| ow ||| (0) ||| (0) ||| 1 1 0.6 0.272727 2.718 w o r ||| w er ||| (0) (1) (1) ||| (0) (1,2) ||| 1 0.1875 1 0.424242 2.718 w ||| w ||| (0) ||| (0) ||| 1 0.75 1 1 2.718

21. Tuning Not a compulsory step but will improve the decoding by a small percentage mkdir tuning; cp $WDIR/corpus/tun.en tuning/input; cp $WDIR/corpus/tun.hi tuning/reference; /usr/share/moses/scripts/training/mert-moses.pl $PWD/tuning/input $PWD/tuning/reference /usr/bin/moses $PWD/model/moses.ini --working- dir $PWD/tuning --rootdir /usr/share/moses/scripts It will take around 1 hour on a server with 32GB RAM

22. Testing mkdir evaluation; /usr/bin/moses -config $WDIR/tuning/moses.ini -input- file $WDIR/corpus/test.en >evaluation/test.output; The output will be in evaluation/test.output file Sample Output h o t hh aa t p h o n e p|UNK hh ow eh n iy b o o k b uw k