Lecture 1, 7/21/2005Natural Language Processing1 CS60057 Speech &Natural Language Processing Autumn...

Lecture 1, 7/21/2005 Natural Language Processing 1

CS60057Speech &Natural Language

Processing

Autumn 2007

Lecture 14b

24 August 2007


LING 180 SYMBSYS 138Intro to Computer Speech and

Language Processing

Lecture 13: Machine Translation (II)

November 9, 2006

Dan Jurafsky

Thanks to Kevin Knight for much of this material, andmany slides also came from Bonnie Dorr andChristof Monz!


Outline for MT Week

Intro and a little history Language Similarities and Divergences Four main MT Approaches

Transfer Interlingua Direct Statistical

Evaluation


Centauri/Arcturan [Knight, 1997]Your assignment, translate this to Arcturan: farok crrrok hihok yorok clok kantok ok-yurp


Centauri/Arcturan [Knight, 1997]

1a. ok-voon ororok sprok .1b. at-voon bichat dat .

7a. lalok farok ororok lalok sprok izokenemok .7b. wat jjat bichat wat dat vat eneat .

2a. ok-drubel ok-voon anok plok sprok.2b. at-drubel at-voon pippat rrat dat .

8a. lalok brok anok plok nok .8b. iat lat pippat rrat nnat .

3a. erok sprok izok hihok ghirok .3b. totat dat arrat vat hilat .

9a. wiwok nok izok kantok ok-yurp .9b. totat nnat quat oloat at-yurp .

4a. ok-voon anok drok brok jok .4b. at-voon krat pippat sat lat .

10a. lalok mok nok yorok ghirok clok .10b. wat nnat gat mat bat hilat .

5a. wiwok farok izok stok .5b. totat jjat quat cat .

11a. lalok nok crrrok hihok yorok zanzanok .11b. wat nnat arrat mat zanzanat .

6a. lalok sprok izok jok stok .6b. wat dat krat quat cat .

12a. lalok rarok nok izok hihok mok .12b. wat nnat forat arrat vat gat .

Your assignment, translate this to Arcturan: farok crrrok hihok yorok clok kantok ok-yurp

Slide from Kevin Knight



1a. ok-voon ororok sprok .

1b. at-voon bichat dat .

7a. lalok farok ororok lalok sprok izok enemok .

7b. wat jjat bichat wat dat vat eneat .

2a. ok-drubel ok-voon anok plok sprok .

2b. at-drubel at-voon pippat rrat dat .

8a. lalok brok anok plok nok .

8b. iat lat pippat rrat nnat .

3a. erok sprok izok hihok ghirok .

3b. totat dat arrat vat hilat .

9a. wiwok nok izok kantok ok-yurp .

9b. totat nnat quat oloat at-yurp .4a. ok-voon anok drok brok jok .

4b. at-voon krat pippat sat lat .

10a. lalok mok nok yorok ghirok clok .

10b. wat nnat gat mat bat hilat .5a. wiwok farok izok stok .

5b. totat jjat quat cat .

11a. lalok nok crrrok hihok yorok zanzanok .

11b. wat nnat arrat mat zanzanat .6a. lalok sprok izok jok stok .

6b. wat dat krat quat cat .

12a. lalok rarok nok izok hihok mok .

12b. wat nnat forat arrat vat gat .




Your assignment, translate this to Arcturan: farok crrrok hihok yorok clok kantok ok-yurpCentauri/Arcturan [Knight, 1997]





















12b. wat nnat forat arrat vat gat .Slide from Kevin Knight

























???











































































Slide from Kevin KnightSlide from Kevin Knight

























???

























Slide from Kevin KnightSlide from Kevin Knight

























process ofelimination

























cognate?



Your assignment, put these words in order: { jjat, arrat, mat, bat, oloat, at-yurp }Centauri/Arcturan [Knight, 1997]






















zerofertility


Clients do not sell pharmaceuticals in Europe => Clientes no venden medicinas en EuropaIt’s Really Spanish/English

1a. Garcia and associates .1b. Garcia y asociados .

7a. the clients and the associates are enemies .7b. los clients y los asociados son enemigos .

2a. Carlos Garcia has three associates .2b. Carlos Garcia tiene tres asociados .

8a. the company has three groups .8b. la empresa tiene tres grupos .

3a. his associates are not strong .3b. sus asociados no son fuertes .

9a. its groups are in Europe .9b. sus grupos estan en Europa .

4a. Garcia has a company also .4b. Garcia tambien tiene una empresa .

10a. the modern groups sell strong pharmaceuticals .10b. los grupos modernos venden medicinas fuertes .

5a. its clients are angry .5b. sus clientes estan enfadados .

11a. the groups do not sell zenzanine .11b. los grupos no venden zanzanina .

6a. the associates are also angry .6b. los asociados tambien estan enfadados .

12a. the small groups are not modern .12b. los grupos pequenos no son modernos .

Slide from Kevin KnightSlide from Kevin KnightSlide from Kevin Knight


Statistical MT Systems

Spanish BrokenEnglish

English

Spanish/EnglishBilingual Text

EnglishText

Statistical Analysis Statistical Analysis

Que hambre tengo yo

What hunger have I,Hungry I am so,I am so hungry,Have I that hunger …

I am so hungry



Statistical MT Systems

Spanish BrokenEnglish

English

Spanish/EnglishBilingual Text

EnglishText

Statistical Analysis Statistical Analysis

Que hambre tengo yo I am so hungry

TranslationModel P(s|e)

LanguageModel P(e)

Decoding algorithmargmax P(e) * P(s|e) e



Three Problems for Statistical MT

Language model Given an English string e, assigns P(e) by formula good English string -> high P(e) random word sequence -> low P(e)

Translation model Given a pair of strings <f,e>, assigns P(f | e) by formula <f,e> look like translations -> high P(f | e) <f,e> don’t look like translations -> low P(f | e)

Decoding algorithm Given a language model, a translation model, and a new

sentence f … find translation e maximizing P(e) * P(f | e)



The Classic Language ModelWord N-Grams

Goal of the language model -- choose among:

He is on the soccer fieldHe is in the soccer field

Is table the on cup theThe cup is on the table

Rice shrineAmerican shrineRice companyAmerican company



Intuition of phrase-based translation (Koehn et al. 2003)

Generative story has three steps

1) Group words into phrases

2) Translate each phrase

3) Move the phrases around


Generative story again

1) Group English source words into phrases e1, e2, …, en

2) Translate each English phrase ei into a Spanish phrase fj.

1) The probability of doing this is (fj|ei)

3) Then (optionally) reorder each Spanish phrase

1) We do this with a distortion probability

2) A measure of distance between positions of a corresponding phrase in the 2 lgs.

3) “What is the probability that a phrase in position X in the English sentences moves to position Y in the Spanish sentence?”


Distortion probability

The distortion probability is parameterized by ai-bi-1

Where ai is the start position of the foreign (Spanish) phrase generated by the ith English phrase ei.

And bi-1 is the end position of the foreign (Spanish) phrase generated by the I-1th English phrase ei-1.

We’ll call the distortion probability d(ai-bi-1). And we’ll have a really stupid model:

d(ai-bi-1) = |ai-bi-1|

Where is some small constant.


Final translation model for phrase-based MT

Let’s look at a simple example with no distortion

P(F | E) ( f i,e ii1

l

)d(ai bi 1)


Phrase-based MT

Language model P(E) Translation model P(F|E)

Model How to train the model

Decoder: finding the sentence E that is most probable


Training P(F|E)

What we mainly need to train is (fj|ei) Suppose we had a large bilingual training corpus

A bitext In which each English sentence is paired with a

Spanish sentence And suppose we knew exactly which phrase in Spanish

was the translation of which phrase in the English We call this a phrase alignment If we had this, we could just count-and-divide:


But we don’t have phrase alignments

What we have instead are word alignments:


Getting phrase alignments

To get phrase alignments:

1) We first get word alignments

2) Then we “symmetrize” the word alignments into phrase alignments


How to get Word Alignments

Word alignment: a mapping between the source words and the target words in a set of parallel sentences.

Restriction: each foreign word comes from exactly 1 English word

Advantage: represent an alignment by the index of the English word that the French word comes from

Alignment above is thus 2,3,4,5,6,6,6


One addition: spurious words

A word in the foreign sentence That doesn’t align with any word in the English sentence Is called a spurious word. We model these by pretending they are generated by an

English word e0:


More sophisticated models of alignment


Computing word alignments: IBM Model 1

For phrase-based machine translation We want a word-alignment To extract a set of phrases A word alignment algorithm gives us P(F,E) We want this to train our phrase probabilities (fj|ei) as

part of P(F|E) But a word-alignment algorithm can also be part of a

mini-translation model itself.


IBM Model 1


IBM Model 1


How does the generative story assign P(F|E) for a Spanish sentence F?

Terminology:

Suppose we had done steps 1 and 2, I.e. we already knew the Spanish length J and the alignment A (and English source E):


Let’s formalize steps 1 and 2

We want P(A|E) of an alignment A (of length J) given an English sentence E

IBM Model 1 makes the (very) simplifying assumption that each alignment is equally likely.

How many possible alignments are there between English sentence of length I and Spanish sentence of length J?

Hint: Each Spanish word must come from one of the English source words (or the NULL word)

(I+1)J

Let’s assume probability of choosing length J is small constant epsilon


Model 1 continued

Prob of choosing a length and then one of the possible alignments:

Combining with step 3:

The total probability of a given foreign sentence F:


Decoding

How do we find the best A?


Training alignment probabilities

Step 1: get a parallel corpus Hansards

Canadian parliamentary proceedings, in French and English Hong Kong Hansards: English and Chinese

Step 2: sentence alignment Step 3: use EM (Expectation Maximization) to train word

alignments


Step 1: Parallel corpora

English German

Diverging opinions about planned tax reform

Unterschiedliche Meinungen zur geplanten Steuerreform

The discussion around the envisaged major tax reform continues .

Die Diskussion um die vorgesehene grosse Steuerreform dauert an .

The FDP economics expert , Graf Lambsdorff , today came out in favor of advancing the enactment of significant parts of the overhaul , currently planned for 1999 .

Der FDP - Wirtschaftsexperte Graf Lambsdorff sprach sich heute dafuer aus , wesentliche Teile der fuer 1999 geplanten Reform vorzuziehen .

Example from DE-News (8/1/1996)

Slide from Christof Monz


Step 2: Sentence Alignment

The old man is happy. He has fished many times. His wife talks to him. The fish are jumping. The sharks await.

Intuition:

- use length in words or chars

- together with dynamic programming

- or use a simpler MT model

El viejo está feliz porque ha pescado muchos veces. Su mujer habla con él. Los tiburones esperan.



Sentence Alignment

1. The old man is happy.

2. He has fished many times.

3. His wife talks to him.

4. The fish are jumping.

5. The sharks await.

El viejo está feliz porque ha pescado muchos veces.

Su mujer habla con él. Los tiburones esperan.



Sentence Alignment

1. The old man is happy.

2. He has fished many times.


4. The fish are jumping.



Su mujer habla con él.

Los tiburones esperan.



Sentence Alignment

1. The old man is happy. He has fished many times.




Su mujer habla con él.

Los tiburones esperan.

Note that unaligned sentences are thrown out, andsentences are merged in n-to-m alignments (n, m > 0).



Step 3: word alignments

It turns out we can bootstrap alignments From a sentence-aligned bilingual corpus We use is the Expectation-Maximization or EM

algorithm


EM for training alignment probs

… la maison … la maison bleue … la fleur …

… the house … the blue house … the flower …

All word alignments equally likely

All P(french-word | english-word) equally likely






“la” and “the” observed to co-occur frequently,so P(la | the) is increased.






“house” co-occurs with both “la” and “maison”, butP(maison | house) can be raised without limit, to 1.0,

while P(la | house) is limited because of “the”

(pigeonhole principle)






settling down after another iteration






Inherent hidden structure revealed by EM training!For details, see:

•Section 24.6.1 in the chapter• “A Statistical MT Tutorial Workbook” (Knight, 1999).• “The Mathematics of Statistical Machine Translation” (Brown et al, 1993)• Software: GIZA++



Statistical Machine Translation



P(juste | fair) = 0.411P(juste | correct) = 0.027P(juste | right) = 0.020 …

new Frenchsentence

Possible English translations,to be rescored by language model



A more complex model: IBM Model 3Brown et al., 1993

Mary did not slap the green witch

Mary not slap slap slap the green witch n(3|slap)

Maria no dió una bofetada a la bruja verde

d(j|i)

Mary not slap slap slap NULL the green witchP-Null

Maria no dió una bofetada a la verde brujat(la|the)

Generative approach:

Probabilities can be learned from raw bilingual text.


How do we evaluate MT? Human tests for fluency

Rating tests: Give the raters a scale (1 to 5) and ask them to rate Or distinct scales for

Clarity, Naturalness, Style Or check for specific problems

Cohesion (Lexical chains, anaphora, ellipsis) Hand-checking for cohesion.

Well-formedness 5-point scale of syntactic correctness

Comprehensibility tests Noise test Multiple choice questionnaire

Readability tests cloze


How do we evaluate MT? Human tests for fidelity

Adequacy Does it convey the information in the original? Ask raters to rate on a scale

Bilingual raters: give them source and target sentence, ask how much information is preserved

Monolingual raters: give them target + a good human translation

Informativeness Task based: is there enough info to do some task? Give raters multiple-choice questions about

content


Evaluating MT: Problems

Asking humans to judge sentences on a 5-point scale for 10 factors takes time and $$$ (weeks or months!)

We can’t build language engineering systems if we can only evaluate them once every quarter!!!!

We need a metric that we can run every time we change our algorithm.

It would be OK if it wasn’t perfect, but just tended to correlate with the expensive human metrics, which we could still run in quarterly.

Bonnie Dorr


Automatic evaluation

Miller and Beebe-Center (1958) Assume we have one or more human translations of the

source passage Compare the automatic translation to these human

translations Bleu NIST Meteor Precision/Recall


BiLingual Evaluation Understudy (BLEU —Papineni, 2001)

Automatic Technique, but …. Requires the pre-existence of Human (Reference) Translations Approach:

Produce corpus of high-quality human translations Judge “closeness” numerically (word-error rate) Compare n-gram matches between candidate translation and

1 or more reference translations

http://www.research.ibm.com/people/k/kishore/RC22176.pdf

Slide from Bonnie Dorr


Reference (human) translation: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport .

Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business [?] and so on electronic mail , which sends out ; The threat will be able after public place and so on the airport to start the biochemistry attack , [?] highly alerts after the maintenance.

BLEU Evaluation Metric(Papineni et al, ACL-2002)

• N-gram precision (score is between 0 & 1)– What percentage of machine n-grams can

be found in the reference translation? – An n-gram is an sequence of n words

– Not allowed to use same portion of reference translation twice (can’t cheat by typing out “the the the the the”)

• Brevity penalty– Can’t just type out single word “the”

(precision 1.0!)

*** Amazingly hard to “game” the system (i.e., find a way to change machine output so that BLEU goes up, but quality doesn’t)



Reference (human) translation: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport .


BLEU Evaluation Metric(Papineni et al, ACL-2002)

• BLEU4 formula (counts n-grams up to length 4)

exp (1.0 * log p1 + 0.5 * log p2 + 0.25 * log p3 + 0.125 * log p4 – max(words-in-reference / words-in-machine – 1, 0)

p1 = 1-gram precisionP2 = 2-gram precisionP3 = 3-gram precisionP4 = 4-gram precision



Reference translation 1: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport .

Reference translation 3: The US International Airport of Guam and its office has received an email from a self-claimed Arabian millionaire named Laden , which threatens to launch a biochemical attack on such public places as airport . Guam authority has been on alert .

Reference translation 4: US Guam International Airport and its office received an email from Mr. Bin Laden and other rich businessman from Saudi Arabia . They said there would be biochemistry air raid to Guam Airport and other public places . Guam needs to be in high precaution about this matter .

Reference translation 2: Guam International Airport and its offices are maintaining a high state of alert after receiving an e-mail that was from a person claiming to be the wealthy Saudi Arabian businessman Bin Laden and that threatened to launch a biological and chemical attack on the airport and other public places .


Multiple Reference Translations

Reference translation 1: The U.S. island of Guam is maintaining a high state of alert after the Guam airport and its offices both received an e-mail from someone calling himself the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport .

Reference translation 3: The US International Airport of Guam and its office has received an email from a self-claimed Arabian millionaire named Laden , which threatens to launch a biochemical attack on such public places as airport . Guam authority has been on alert .

Reference translation 4: US Guam International Airport and its office received an email from Mr. Bin Laden and other rich businessman from Saudi Arabia . They said there would be biochemistry air raid to Guam Airport and other public places . Guam needs to be in high precaution about this matter .

Reference translation 2: Guam International Airport and its offices are maintaining a high state of alert after receiving an e-mail that was from a person claiming to be the wealthy Saudi Arabian businessman Bin Laden and that threatened to launch a biological and chemical attack on the airport and other public places .




Bleu Comparison

Chinese-English Translation Example:

Candidate 1: It is a guide to action which ensures that the military always obeys the commands of the party.

Candidate 2: It is to insure the troops forever hearing the activity guidebook that party direct.

Reference 1: It is a guide to action that ensures that the military will forever heed Party commands.

Reference 2: It is the guiding principle which guarantees the military forces always being under the command of the Party.

Reference 3: It is the practical guide for the army always to heed the directions of the party.



How Do We Compute Bleu Scores? Intuition: “What percentage of words in candidate occurred in some

human translation?” Proposal: count up # of candidate translation words (unigrams) # in

any reference translation, divide by the total # of words in # candidate translation

But can’t just count total # of overlapping N-grams! Candidate: the the the the the the Reference 1: The cat is on the mat

Solution: A reference word should be considered exhausted after a matching candidate word is identified.



“Modified n-gram precision”

For each word compute: (1) total number of times it occurs in any single reference translation(2) number of times it occurs in the candidate translation

Instead of using count #2, use the minimum of #2 and #2, I.e. clip the counts at the max for the reference transcription

Now use that modified count. And divide by number of candidate words.



Modified Unigram Precision: Candidate #1




It(1) is(1) a(1) guide(1) to(1) action(1) which(1) ensures(1) that(2) the(4) military(1) always(1) obeys(0) the commands(1) of(1) the party(1)

What’s the answer???

17/18



Modified Unigram Precision: Candidate #2

It(1) is(1) to(1) insure(0) the(4) troops(0) forever(1) hearing(0) the activity(0) guidebook(0) that(2) party(1) direct(0)

What’s the answer????

8/14






Modified Bigram Precision: Candidate #1

It is(1) is a(1) a guide(1) guide to(1) to action(1) action which(0) which ensures(0) ensures that(1) that the(1) the military(1) military always(0) always obeys(0) obeys the(0) the commands(0) commands of(0) of the(1) the party(1)


10/17






Modified Bigram Precision: Candidate #2

Reference 1: It is a guide to action that ensures that themilitary will forever heed Party commands.



It is(1) is to(0) to insure(0) insure the(0) the troops(0) troops forever(0) forever hearing(0) hearing the(0) the activity(0) activity guidebook(0) guidebook that(0) that party(0) party direct(0)


1/13



Catching Cheaters

Reference 1: The cat is on the mat

Reference 2: There is a cat on the mat

the(2) the the the(0) the(0) the(0) the(0)

What’s the unigram answer?

2/7

What’s the bigram answer?

0/7



Bleu distinguishes human from machine translations



Bleu problems with sentence length

Candidate: of the

Solution: brevity penalty; prefers candidates translations which are same length as one of the references

Reference 1: It is a guide to action that ensures that themilitary will forever heed Party commands.



Problem: modified unigram precision is 2/2, bigram 1/1!



BLEU Tends to Predict Human Judgments

R2 = 88.0%

R2 = 90.2%

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Human Judgments

NIS

T S

co

re

Adequacy

Fluency

Linear(Adequacy)Linear(Fluency)

slide from G. Doddington (NIST)

(va

ria

nt

of

BL

EU

)


Summary

Intro and a little history Language Similarities and Divergences Four main MT Approaches

Transfer Interlingua Direct Statistical

Evaluation


Classes

LINGUIST 139M/239M. Human and Machine Translation. (Martin Kay)

CS 224N. Natural Language Processing (Chris Manning)

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