CS460/626 : Natural Language Processing/Speech, NLP and the Web

(Lecture 18– Alignment in SMT and Tutorial on Giza++ and Moses)

Pushpak BhattacharyyaCSE Dept., IIT Bombay

15th Feb, 2011

Going forward from word alignment

Word alignment

Phrase Alignment Decoding(going to bigger units (best possibleOf correspondence) translation)

Abstract ProblemGiven: eoe1e2e3….enen+1 (Entities)

Goal: lol1l2l3….lnln+1 (Labels)

The Goal is to find the best possible label sequence

Generative Model

))|((maxarg* ELPLL

)|().(maxarg)|(maxarg LEPLPELPL

SimplificationUsing Markov Assumption, the Language Model can be represented using bigrams

Similarly translation model can also be represented in the following way:

)|()( 10

ii

n

iLLPLP

n

iii lePLEP

0

)|()|(

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

)|().(maxarg)|(maxarg* EFPEPFEPeE

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 ? आप कि�स �े_साथ जाना चाहते_हो (Aap kis ke_sath jaana chahate_ho)

who who

do do and so on you youwant want

to_go to_gowith with

Each word have multiple translation options.

Preposition Stranding

Column of words of target language on the source language words

^ Aap kis ke_sath jaana chahate_ho .

who who do do and so on you you^ want want …

. to_go to_go with with

Find the best possible path from ‘^’ to ‘.’ using transition andObservation probabilities.

Viterbi can be used

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

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

AlignmentA function from target position to source position:

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The alignment sequence is: 2,3,4,5,6,6,6Alignment 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

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Training Alignment Models

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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

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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

Limitation: Only 1->Many Alignments allowed

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Phrase-based alignment

More natural

Many-to-one mappings allowed

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

Steps

Input - sentence aligned parallel corpus

Output- target side tagged data Training Tuning Generate output on test corpus

(decoding)

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;

Example train.enh e l l oh e l l ow o r l dc o m p o u n d w o r dh y p h e n a t e do n eb o o mk w e e z l e b o t t e r

train.prhh eh l owhh ah l oww er l dk aa m p aw n d w er dhh ay f ah n ey t ih dow eh n iyb uw mk w iy z l ah b aa t ah r

Sample from Phrase-tableb o ||| b aa ||| (0) (1) ||| (0) (1) ||| 1 0.666667 1

0.181818 2.718b ||| b ||| (0) ||| (0) ||| 1 1 1 1 2.718c 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 2.718c ||| p ||| (0) ||| (0) ||| 1 1 1 1 2.718d w ||| d w ||| (0) (1) ||| (0) (1) ||| 1 0.75 1 1 2.718d ||| d ||| (0) ||| (0) ||| 1 1 1 1 2.718e b ||| ah b ||| (0) (1) ||| (0) (1) ||| 1 1 1 0.6 2.718e l l ||| ah l ||| (0) (1) (1) ||| (0) (1,2) ||| 1 1 0.5 0.5

2.718e l l ||| eh l ||| (0) (0) (1) ||| (0,1) (2) ||| 1 0.111111

0.5 0.111111 2.718e l ||| eh ||| (0) (0) ||| (0,1) ||| 1 0.111111 1

0.133333 2.718e ||| ah ||| (0) ||| (0) ||| 1 1 0.666667 0.6 2.718h e ||| hh ah ||| (0) (1) ||| (0) (1) ||| 1 1 1 0.6 2.718h ||| hh ||| (0) ||| (0) ||| 1 1 1 1 2.718l e b ||| l ah b ||| (0) (1) (2) ||| (0) (1) (2) ||| 1 1 1

0.5 2.718l 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 2.718l l ||| l ||| (0) (0) ||| (0,1) ||| 0.25 1 1 0.833333 2.718l o ||| l ow ||| (0) (1) ||| (0) (1) ||| 0.5 1 1 0.227273 2.718l ||| l ||| (0) ||| (0) ||| 0.75 1 1 0.833333 2.718m ||| m ||| (0) ||| (0) ||| 1 0.5 1 1 2.718n d ||| n d ||| (0) (1) ||| (0) (1) ||| 1 1 1 1 2.718n e ||| eh n iy ||| (1) (2) ||| () (0) (1) ||| 1 1 0.5 0.3 2.718n e ||| n iy ||| (0) (1) ||| (0) (1) ||| 1 1 0.5 0.3 2.718n ||| eh n ||| (1) ||| () (0) ||| 1 1 0.25 1 2.718o o m ||| uw m ||| (0) (0) (1) ||| (0,1) (2) ||| 1 0.5 1 0.181818 2.718o o ||| uw ||| (0) (0) ||| (0,1) ||| 1 1 1 0.181818 2.718o ||| aa ||| (0) ||| (0) ||| 1 0.666667 0.2 0.181818 2.718o ||| ow eh ||| (0) ||| (0) () ||| 1 1 0.2 0.272727 2.718o ||| ow ||| (0) ||| (0) ||| 1 1 0.6 0.272727 2.718w o r ||| w er ||| (0) (1) (1) ||| (0) (1,2) ||| 1 0.1875 1 0.424242 2.718w ||| w ||| (0) ||| (0) ||| 1 0.75 1 1 2.718

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

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