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

transcript

Machine Translation

Om Damani(Ack: Material taken from

JurafskyMartin 2nd Ed., Brown et. al. 1993)

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

The spirit is willing but the flesh is weak

Google English-Hindi Translation System

आत्मा� पर शर�र दुर्ब�ल है�

Google Hindi-English Translation System

Spirit on the flesh is weak

State of the Art (English-Hindi) – March 19, 2009

Is state of the art so bad

कल� क� है�लत इतनी� खर�र्ब है�

The state of the art is so bad

Is State of the Art (English-Hindi) so bad

State of the english hindi translation is not so bad

र�ज्य क� अं�ग्रे�ज़ी� हिहैन्दी� अंनी वा�दी क� इतनी� र्ब र� नीहै" है�

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.OK. Maybe it is __ bad.

State of the English Hindi translation is not so bad

र�ज्य मा# अं�ग्रे�जी� से� हिंहै'दी� अंनी वा�दी क� इतनी� र्ब र� नीहै" है�

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.OK. Maybe it is __ __ bad.

र�ज्य क� अं�ग्रे�ज़ी� हिहैन्दी� अंनी वा�दी क� इतनी� र्ब र� नीहै" है�

Your Approach to Machine Translation

Translation Approaches

Direct Transfer – What Novices do

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 र्ब�गा�ल� कहिवाय* नी� इसे भू,मिमा क� गा�त गा�ए है/Kai Bangali kaviyon ne is bhoomi ke geet gaaye hain

Morph: कई र्ब�गा�ल� कहिवा-PL,OBL नी� इसे भू,मिमा क� गा�त { गा�ए है�}-PrPer,PlKai Bangali kavi-PL,OBL ne is bhoomi ke geet {gaaye hai}-PrPer,Pl

Syntax Transfer (Analysis-Transfer-Generation)

Here phrases NP, VP etc. can be arbitrarily large

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

Interlingua Based Transfer

farmer

agtobj

contact

orregion

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 N2

In practice: Amazingly complex system to tackle N2 language pairs

Difficulties in Translation – Language Divergence (Concepts from Dorr 1993, Text/Figures from Dave, Parikh and

Bhattacharyya 2002)

Constituent Order Prepositional Stranding Null Subject

Conflational Divergence Categorical Divergence

Lost in Translation: We are talking mostly about syntax, not semantics, or pragmatics

You: Could you give me a glass of waterRobot: 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

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

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 translationsConsider all possible permutations of each combination

And somehow select the best partition/translation/permutation

कई र्ब�गा�ल� कहिवाय* नी� इसे भू,मिमा क� गा�त गा�ए है/Kai Bangali kaviyon ne is bhoomi ke geet gaaye hain

कई र्ब�गा�ल� कहिवाय* नी� इसे भू,मिमा क� गा�त गा�ए है/

Many Bengali Poets this land of have sung poem

Several Bengali to this place ‘s sing songs

Many poets from Bangal

in this space song sung

Poets from Bangladesh

farm have sung songs

To this space have sung songs of many poets from Bangal

How many combinations are we talking about

Number of choices for a N word sentence

N=20 ??

Number of possible chess games

How do we get the Phrase TableCollect 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 sideSomehow assign the probability to each phrase pair

इसेक� लिलए आप मा�चर क्षे�त्र क� हिकसे�नी* से5 से�पक� क�जिजीए

For this you contact the farmers of Manchar region

Data Sparsity Problems in Creating Phrase Table

Sunil is eating mangoe -> Sunil aam khata haiNoori is eating banana -> Noori kela khati haiSunil 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

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

P(F|E) vs. P(E|F)

P(F|E) is the translation probability – we need to look at the generationprocess by which <F,E> 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 toensure 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( र्ब�रिरश है8 रहै� है�|It is raining) = .02P( र्बरसे�त आ रहै� है�| It is raining) = .03P( र्ब�रिरश है8 रहै� है�|rain is happening) = .420

We need to estimate P(It is raining| र्ब�रिरश है8 रहै� है�) vs. P(rain is happening| र्ब�रिरश है8 रहै� है�)

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

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 TE is generated from E; TE is transformed to TF;

finally TF is converted into F Can you write the mathematical expression

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

Generation Example: Alignment

Simplify Generation: Only 1->Many Alignments allowed

AlignmentA function from target position to source position:

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

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

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

Documents