Machine Translation and MT tools: Giza++ and Moses -Nirdesh Chauhan

Outline Problem statement in SMT Translation models Using Giza++ and Moses

Introduction to SMT Given a sentence in foreign language F, find most appropriate translation in English E P(F|E) – Translation model P(E) – Language model

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 Reordering: permute the set of all foreign words - words possibly moving across phrase boundaries We need the notion of alignment to better explain mathematic behind the generation process

Alignment

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

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

IBM Model 1: Generative Process

IBM Model 1: Details No assumptions. Above formula is exact. Choosing length: P(J|E) = P(J|E,I) = P(J|I) = Choosing Alignment: all alignments equiprobable Translation Probability

Training Alignment Models 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(ai|ai-1,I) How to compute these probabilities if all you have is a parallel corpora

Intuition : Interdependence of Probabilities 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

Expectation Maximization (EM) Algorithm Used when we want maximum likelihood estimate of the parameters of a model when the model depends on hidden variables In present case, parameters are Translation Probabilities, and hidden Variables are alignment probabilities Init: Start with an arbitrary estimate of parameters E-step: compute the expected value of hidden variables M-Step: Recompute the parameters that maximize the likelihood of data given the expected value of the hidden variables from E-step

Example of EM Algorithm Green house Casa verde The house La case Init: Assume that any word can generate any word with equal prob: P(la|house) = 1/3

E-Step E-Step:

M-Step

E-Step again 1/3 2/3 2/3 1/3 Repeat till convergence

Limitation: Only 1->Many Alignments allowed

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

Generating Bi-directional Alignments Existing models only generate uni-directional alignments Combine two uni-directional alignments to get many-to-many bi- directional alignments

Hindi-Eng Alignment | छुट्टियों के लिए गोवा एक प्रमुख समुद्र-तटीय गंतव्य है Goa | is a premier beach vacation destination

Eng-Hindi Alignment | छुट्टियों के लिए गोवा एक प्रमुख समुद्र-तटीय गंतव्य है Goa | is a premier beach vacation destination

Combining Alignments + | P=4/5=.8,R=4/7=.6 P=2/3=.67, R=2/7=.3 छुट्टियों के लिए गोवा एक प्रमुख समुद्र-तटीय गंतव्य है Goa + is a premier | beach vacation destination P=4/5=.8,R=4/7=.6 P=2/3=.67, R=2/7=.3 P=5/6=.83,R=5/7=.7 P=6/9=.67,R=6/7=.85

A Different Heuristic from Moses-Site GROW-DIAG-FINAL(e2f,f2e): neighboring = ((-1,0),(0,-1),(1,0),(0,1),(-1,-1),(-1,1),(1,-1),(1,1)) alignment = intersect(e2f,f2e); GROW-DIAG(); FINAL(e2f); FINAL(f2e); GROW-DIAG(): iterate until no new points added for english word e = 0 ... en for foreign word f = 0 ... fn if ( e aligned with f ) for each neighboring point ( e-new, f-new ): if (( e-new, f-new ) in union( e2f, f2e ) and ( e-new not aligned and f-new not aligned )) add alignment point ( e-new, f-new ) FINAL(a): for english word e-new = 0 ... en for foreign word f-new = 0 ... fn if ( ( ( e-new, f-new ) in alignment a) and ( e-new not aligned or f-new not aligned ) ) Proposed Changes: After growing diagonal Align the shorter sentence first And use alignments only from corresponding directional alignment

Generating Phrase Alignments छुट्टियों के लिए गोवा एक प्रमुख समुद्र-तटीय गंतव्य है Goa + is a premier beach vacation destination a premier beach vacation destination एक प्रमुख समुद्र-तटीय गंतव्य है premier beach vacation प्रमुख समुद्र-तटीय

Using Moses and Giza++ Refer to http://www.statmt.org/moses_steps.html

Steps Install all packages in Moses Input - sentence aligned parallel corpus Training Tuning Generate output on test corpus (decoding)

Example train.pr train.en h e l l o hh eh l ow hh ah l ow 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

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

Testing 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