Microsoft PowerPoint - cs626-460-lect10-and-11-MT-approaches-2011-1-25-27.pptx(Lecture 10 11 MT approaches)(Lecture 10, 11–MT approaches)
Pushpak BhattacharyyaPushpak Bhattacharyya CSE Dept., IIT Bombay
25th J d 27th J 201125th Jan and 27th Jan, 2011
Acknowledgement: parts are from Hansraj’s dual degree seminar presentation
Czeck-English data
[nesu] “I carry” [ponese] “He will carry”[ponese] He will carry [nese] “He carries” [ ] “ h ”[nesou] “They carry” [yedu] “I drive” [plavou] “They swim”
To translate …
I will carry. They driveThey drive. He swims. h ll dThey will drive.
Hindi-English data
[DhotA huM] “I carry” [DhoegA] “He will carry”[DhoegA] He will carry [DhotA hAi] “He carries” [ h h ] “ h ”[Dhote hAi] “They carry” [chalAtA huM] “I drive” [tErte hEM] “They swim”
Bangla-English data
[bai] “I carry” [baibe] “He will carry”[baibe] He will carry [bay] “He carries” [b ] “ h ”[bay] “They carry” [chAlAi] “I drive” [sAMtrAy] “They swim”
MT Approaches interlinguainterlingua
Example Based MT (EBMT)
Motivation
MT: NLP Complete NLP: AI complete AI: CS completeAI: CS complete How will the world be different when the language barrier  disappears? V l f t t i d t b t l t d tl dVolume of text required to be translated currently exceeds  translators’ capacity (demand outstrips supply).
Solution: automation (the only solution) M hi t l ti t h iMany machine translation techniques
Which approach is better for HindiEnglish MT
Interlingual representation: complete disambiguation
@ t <is-a >
@past action
<is-a > person
Kinds of disambiguation needed for a complete and correctfor a complete and correct interlingua graph
N: Name P: POSP: POS A: Attachment S: Sense C: Co-reference R: Semantic Role
Target Sentence Generation from interlingua
Target Sentence Generation
Morphological Synthesis
(Word/Phrase Translation )
Washington ne Washington koWashingtonagent ne Washingtonobject ko sattaagoal ke liye chunaa
Vote- chunna Power- sattaa
Statistical Machine Translation (SMT)
Data driven approach Goal is to find out the English sentence e given foreign  language sentence f whose p(e|f) is maximum.
Translations are generated on the basis of statistical modelTranslations are generated on the basis of statistical model Parameters are estimated using bilingual parallel corpora
SMT: Language Model
To detect good English sentences Probability of an English sentence s1s2 …… sn  can be written  asas
Pr(s1s2 …… sn) = Pr(s1) * Pr(s2|s1) *. . . * Pr(sn|s1 s2 . . . sn­1)
Here Pr(sn|s1 s2 . . . sn­1) is the probability that word sn follows  d t iword string s1 s2 . . . sn­1.  Ngram model probability
Trigrammodel probability calculationTrigram model probability calculation
SMT: Translation Model
P(f|e): Probability of some f given hypothesis English  translation e How to assign the values to p(e|f) ?How to assign the values to p(e|f) ?
Sentences are infinite not possible to find pair(e f) for all sentences
Sentence level
Sentences are infinite, not possible to find pair(e,f) for all sentences
Introduce a hidden variable a, that represents alignments  b t th i di id l d i th t ibetween the individual words in the sentence pair
Word level
Alignment If the string, e= e1
l= e1 e2 …el, has l words, and the string, f= f1m=f1f2...fm, has m words, then the alignment, a, can be represented by a series, a1
m= a1a2...am , of m values, each between 0 and l such that if the word in position j of the p j f-string is connected to the word in position i of the e-string, then
a = i andaj= i, and if it is not connected to any English word, then aj= O
Example of alignmentp g
English: Ram went to schoolEnglish: Ram went to school Hindi: Raama paathashaalaa gayaa
Ram went to school
Translation Model: Exact expression
Choose alignment  i d
d l f h [ ]
given e and m of foreign word  given e, m, a
of foreign language  string given e
Five models for estimating parameters in the expression [2]
Model1, Model2, Model3, Model4, Model5
Proof of Translation Model: Exact expression
∑= eafef )|,Pr()|Pr(
= j
)|P (f )|P ( ∏ −−− m
m is fixed for a particular f, hence
)|,,Pr( emaf )|Pr( em= ∏ =j
jj j
Simplest model Assumptions
Pr(m|e) is independent of m and e and is equal to ε Alignment of foreign language words (FLWs) depends only on  length of English sentence
= (l+1)-1( ) l is the length of English sentence
The likelihood function will be
M i i th lik lih d f ti t i d tMaximize the likelihood function constrained to 
Model-1: Parameter estimation
Using Lagrange multiplier for constrained maximization,  the solution for model1 parameters
λe : normalization constant; c(f|e; f,e) expected count; δ(f,fj)  is 1 if f & f are same zero otherwiseis 1 if f & fj are same, zero otherwise.
Estimate t(f|e) using Expectation Maximization (EM)      procedurep
Model-2
Alignment of  FLW to an Eng word depends on its position
The likelihood function is
Model1 & 2 Model1 is special case of model2 where To instantiate the model2 parameters, use parameter  estimated in model1
Model-3
Fertility: Number of FLWs to which an Eng word is connected  in a randomly selected alignment Tablet: List of FLWs connected to an Eng wordg Tableau: The collection of tablets
The alignment processThe alignment process for each English word
Begin Decide the fertility of the wordy Get a list of French words to connect to the word
End Permute words in tableau to generate f
Model-3: Example
English Sentence (e) = Annual inflation rises to 11.42%
Step1:  Deciding  fertilities(F) e = Annual  inflation                                     rises                       to   11.42% F = Annual  inflation inflation inflation   rises rises rises     to   11.42%
Model-3: Example
English Sentence (e) = Annual inflation rises to 11.42%
Step2:  Translation to FLWs(T) e = Annual  inflation                                    rises                       to   11.42% F = Annual  inflation inflation inflation   rises rises rises     to   11.42% T= 11.42%
Model-3: Example
English Sentence (e) = Annual inflation rises to 11.42%
Step 3: Reordering FLWs(R)Step3:  Reordering FLWs(R) e = Annual  inflation                                    rises                       to   11.42% F = Annual  inflation inflation inflation   rises rises rises     to   11.42% T = 11 42%T =  11.42% R =  11.42%    
Values fPr, T, R  calculated using the formulas obtained in model3 [2]
Model-4 & 5
Model3: Every word is moved independently Model4: Consider phrases (cept) in a sentence Distortion probability is replaced by
A parameter for head of the each cept A parameter for the remaining part of the ceptp g p p
Deficiency in model3 & 4 In distortion probability
fModel­5 removes the deficiency Avoid unavailable positions Introduces a new variable for the positions
Example Based Machine Translation (EBMT)
Basic idea: translate a sentence by using the closest  match in parallel data
Inspired by human analogical thinkingInspired by human analogical thinking
Issues Related to Examples in Corpora
Granularity of examples Parallel text should be aligned at the subsentence level
Number of examplesNumber of examples Suitability of examples
(i) Columbus discovered America (ii) America was discovered by Columbus ( ) Ti fli lik (b) Ti fli lik(a) Time flies like an arrow  (b) Time flies like an arrow
How examples should be stored?p Annotated tree structure Generalized examples
“Rajesh will reach Mumbai by 10:00 pm”­>”P will reach D by T”
Annotated Tree Structure: example
EBMT: Matching and Retrieval (1/2)
System must be able to recognize the similarity and  differences b/w the input and stored examples
String based matching: Longest common subsequence Takes word similarity into account while sense disabiguation
EBMT: Matching and Retrieval (2/2)
Angle of similarity: Trigonometric similarity measure based on relative length &  relative contents (x). Select ‘Symbol’ in the Insert menu. (y). Select ‘Symbol’ in the Insert menu to enter a character from the symbol set. (z). Select ‘Paste’ in the Edit menu. (w) Select ‘Paste’ in the Edit menu to enter some text from the clip board(w). Select  Paste  in the Edit menu to enter some text from the clip board.
θxy: the qualitative difference between sentence x and y δ(x,y): the difference between size of x and y,
EBMT: Adaptation & Recombination
Adaptation Extracting appropriate fragments from the matched translation
The boy entered  the house>   I saw a tiger >    The boy eats his breakfast  >   I saw the boy                           >  
Boundary FrictionBoundary Friction Retrieved translations do not fit the syntactic context
I saw the boy                           >  * 
Recombine fragments into target text SMT “language model” can be used
Interlingua Based MT
Interlingua "between languages“
SL text converted into a languageindependent or 'universal'  b h f l TLabstract representation then transform into several TL
Universal Networking Language (UNL)
UNL is an example of interlingua Represents information sentence by sentence UNL i d fUNL is composed of
Universal words Relations 
{/unl}
Issues related to interlingua
Interlingua must Capture the knowledge in text precisely and accurately Handle cross language divergenceHandle cross language divergence
Divergence between HindiEnglish language Constituent order divergence Null subject divergenceNull subject divergence
== * am going (I am going) Conflational divergence
== Jim stabbed John Promotional divergence
The play is on == 
Benefits & Shortcomings(1/3)
Statistical Machine translation “Every time I fire a linguist, my system’s performance improves”
(Brown et al. 1988)(Brown et al. 1988) Pros
No linguistic knowledge is required Great deal of natural language in machine readable textGreat deal of natural language in machine readable text
Loose dependencies  b/w languages can be modeled better Cons
Probability of rare words can’t be trustedProbability of rare words can t be trusted Not good for idioms, jokes, compound words, text having hidden  meaning Selection of correct morphological word is difficult
Benefits & Shortcomings(2/3)
Example Based MT Pros
P f t t l ti f t if i il f d iPerfect translation of a sentence if very similar one found in  example sentences No need to bother about previously translated sentences
Cons Fails if no match found in corpora Problem at points of example concatenation in recombination step
Benefits & Shortcomings(3/3)
Interlingua based MT Pros
Add a ne lang age and get all a s translation to all pre io slAdd a new language and get allways translation to all previously  added languages Monolingual lingual development team Economical in situation where translation among multiple languages g p g g is used
Cons “Meaning” is arbitrarily deep. At what level of detail do we stop?g y p p Human development time
Translation is Ubiquitous
Between Languages Delhi is the capital of IndiaDelhi is the capital of India
Between dialectsBetween dialects Example next slide
B t i tBetween registers My “mom” not well. My “mother” is unwell (in a leave application)
Between dialects (1/3)Between dialects (1/3) Lage Raho Munnabhai: an excellent example Scene: Munnabhai (Sanjay Dutt) is Prof. Murli Prasad Sharma being interviewed with some iti en king q e tion in p e en e of J hn icitizens asking questions in presence of Jahnavi
(Vidya Baalan) Question by citizen:Question by citizen: , . .
Between dialects (2/3)Between dialects (2/3) Bapu from behind invisible to others:

Munnabhai
Bapu
Munnabhai full country
Bapu
MunnabhaiMunnabhai
Between dialects (3/3)Between dialects (3/3) Bapu
Munnabhai ,
Bapu
Munnabhai , ,
, heart heart !
Comparison b/w SMT, EBMT, InterlinguaComparison b/w SMT, EBMT, Interlingua
Property Example Based MT Statistical MT Interlingua based MT
Parallel Yes Yes No
Corpora Yes Yes No
Dictionary Yes No Yes
Parser Yes No Yes
1. P. Brown, S. Della Pietra, V. Della Pietra, and R. Mercer. The mathematics  of statistical machine translation: parameter estimation. Computational  Linguistics, 19(2), 263311.  (1993)
2. Makoto Nagao. A framework of a mechanical translation between Japanese  and English by analogy principle, in A. Elithorn and R. Banerji: Artificial  and Human Intelligence. Elsevier Science Publishers. (1984).
3. Somers H. Review Article: Example based Machine Translation. Machine  Translation, Volume 14, Number 2, pp. 113157(45). (June 1999)
4. D. Turcato, F. Popowich. What is Example­Based Machine Translation? In  M. Carl and A. Way (eds). Recent Advances of EBMT. Kluwer Adacemic Publishers, Dordrecht. Note, revised version of Workshop Paper.  (2003)
References (2/2)
5. Dave S., Parikh J. and Bhattacharyya. Interlingua Based English Hindi Machine Translation and Language Divergence. P. Journal of Machine Translation, Volume 17. (2002)
6 Adam L Be ge Stephen A Della Piet a Y Vincent J Della Piet a Y A6. Adam L. Berger, Stephen A. Della Pietra Y, Vincent J. Della Pietra Y. A maximum entropy approach to natural language processing. Computational Linguistics, (22-1), (March 1996).
7. Jason Baldridge, Tom Morton, and Gann Bierner. The opennlp.maxent package: g , , p p p g POS tagger, end of sentence detector, tokenizer, name finder. http://maxent.sourceforge.net/ version- 2.4.0 (Oct. 2005)
8. Universal Networking Language (UNL) Specifications. UNL Center of UNDL Foundation URL: http://www undl org/unlsys/unl/unl2005/ 7 June 2005Foundation. URL: http://www.undl.org/unlsys/unl/unl2005/. 7 June 2005.