Introduction to MT
Ling 580
Fei Xia
Week 1: 1/03/06
Outline
• Course overview
• Introduction to MT
– Major challenges
– Major approaches
– Evaluation of MT systems
• Overview of word-based SMT
Course overview
General info
• Course website:
– Syllabus (incl. slides and papers): updated every week.
– Message board
– ESubmit
• Office hour: Fri: 10:30am-12:30pm.
• Prerequisites:
– Ling570 and Ling571.
– Programming: C or C++, Perl is a plus.
– Introduction to probability and statistics
Expectations
• Reading:
– Papers are online
– Finish reading before class. Bring your questions to
class.
• Grade:
–
–
–
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Leading discussion (1-2 papers): 50%
Project: 40%
Class participation: 10%
No quizzes, exams
Leading discussion
•
•
•
•
Indicate your choice via EPost by Jan 8.
You might want to read related papers.
Make slides with PowerPoint.
Email me your slides by 3:30am on the
Monday before your presentation.
• Present the paper in class and lead the
discussion: 40-50 minutes.
Project
• Details will be available soon.
• Project presentation: 3/7/06
• Final report: due on 3/12/06
• Pongo account will be ready soon.
Introduction to MT
A brief history of MT
(Based on work by John Hutchins)
• Before the computer: In the mid 1930s, a FrenchArmenian Georges Artsrouni and a Russian Petr
Troyanskii applied for patents for ‘translating machines’.
• The pioneers (1947-1954): the first public MT demo was
given in 1954 (by IBM and Georgetown University).
• The decade of optimism (1954-1966): ALPAC (Automatic
Language Processing Advisory Committee) report in
1966: "there is no immediate or predictable prospect of
useful machine translation."
A brief history of MT (cont)
• The aftermath of the ALPAC report (19661980): a virtual end to MT research
• The 1980s: Interlingua, example-based
MT
• The 1990s: Statistical MT
• The 2000s: Hybrid MT
Where are we now?
• Huge potential/need due to the internet,
globalization and international politics.
• Quick development time due to SMT, the
availability of parallel data and computers.
• Translation is reasonable for language pairs with
a large amount of resource.
• Start to include more “minor” languages.
What is MT good for?
•
•
•
•
Rough translation: web data
Computer-aided human translation
Translation for limited domain
Cross-lingual IR
• Machine is better than human in:
– Speed: much faster than humans
– Memory: can easily memorize millions of word/phrase
translations.
– Manpower: machines are much cheaper than humans
– Fast learner: it takes minutes or hours to build a new system.
Erasable memory 
– Never complain, never get tired, …
Major challenges in MT
Translation is hard
•
•
•
•
Novels
Word play, jokes, puns, hidden messages
Concept gaps: go Greek, bei fen
Other constraints: lyrics, dubbing, poem,
…
Major challenges
• Getting the right words:
– Choosing the correct root form
– Getting the correct inflected form
– Inserting “spontaneous” words
• Putting the words in the correct order:
– Word order: SVO vs. SOV, …
– Unique constructions:
– Divergence
Lexical choice
• Homonymy/Polysemy: bank, run
• Concept gap: no corresponding concepts in
another language: go Greek, go Dutch, fen sui,
lame duck, …
• Coding (Concept  lexeme mapping)
differences:
– More distinction in one language: e.g., kinship
vocabulary.
– Different division of conceptual space:
Choosing the appropriate inflection
• Inflection: gender, number, case, tense, …
• Ex:
– Number: Ch-Eng: all the concrete nouns:
ch_book  book, books
– Gender: Eng-Fr: all the adjectives
– Case: Eng-Korean: all the arguments
– Tense: Ch-Eng: all the verbs:
ch_buy  buy, bought, will buy
Inserting spontaneous words
•
Function words:
–
Determiners: Ch-Eng:
ch_book  a book, the book, the books, books
–
Prepositions: Ch-Eng:
… ch_November  … in November
–
Relative pronouns: Ch-Eng:
… ch_buy ch_book de ch_person  the person who bought /book/
–
Possessive pronouns: Ch-Eng:
ch_he ch_raise ch_hand  He raised his hand(s)
–
Conjunction: Eng-Ch:
Although S1, S2  ch_although S1, ch_but S2
–
…
Inserting spontaneous words (cont)
• Content words:
– Dropped argument: Ch-Eng:
ch_buy le ma  Has Subj bought Obj?
– Chinese First name: Eng-Ch:
Jiang …  ch_Jiang ch_Zemin …
– Abbreviation, Acronyms: Ch-Eng:
ch_12 ch_big  the 12th National Congress of the
CPC (Communist Party of China)
– …
Major challenges
• Getting the right words:
– Choosing the correct root form
– Getting the correct inflected form
– Inserting “spontaneous” words
• Putting the words in the correct order:
– Word order: SVO vs. SOV, …
– Unique construction:
– Structural divergence
Word order
• SVO, SOV, VSO, …
• VP + PP  PP VP
• VP + AdvP  AdvP + VP
• Adj + N  N + Adj
• NP + PP  PP NP
• NP + S  S NP
• P + NP  NP + P
“Unique” Constructions
• Overt wh-movement: Eng-Ch:
– Eng: Why do you think that he came yesterday?
– Ch: you why think he yesterday come ASP?
– Ch: you think he yesterday why come?
• Ba-construction: Ch-Eng
– She ba homework finish ASP  She finished her
homework.
– He ba wall dig ASP CL hole  He digged a hole in
the wall.
– She ba orange peel ASP skin  She peeled the
orange’s skin.
Translation divergences
• Source and target parse trees
(dependency trees) are not identical.
• Example: I like Mary  S: Marta me
gusta a mi (‘Mary pleases me’)
• More discussion next time.
Major approaches
How humans do translation?
• Learn a foreign language:
– Memorize word translations
– Learn some patterns:
– Exercise:
• Passive activity: read, listen
• Active activity: write, speak
• Translation:
– Understand the sentence
– Clarify or ask for help (optional)
– Translate the sentence
Training stage
Translation lexicon
Templates, transfer rules
Reinforced learning?
Reranking?
Decoding stage
Parsing, semantics analysis?
Interactive MT?
Word-level? Phrase-level?
Generate from meaning?
What kinds of resources are
available to MT?
• Translation lexicon:
– Bilingual dictionary
• Templates, transfer rules:
– Grammar books
• Parallel data, comparable data
• Thesaurus, WordNet, FrameNet, …
• NLP tools: tokenizer, morph analyzer, parser, …
 More resources for major languages, less for “minor”
languages.
Major approaches
•
•
•
•
•
Transfer-based
Interlingua
Example-based (EBMT)
Statistical MT (SMT)
Hybrid approach
The MT triangle
Meaning
(interlingua)
Transfer-based
Phrase-based SMT, EBMT
Word-based SMT, EBMT
word
Word
Transfer-based MT
•
Analysis, transfer, generation:
1.
2.
3.
4.
•
Resources required:
–
–
–
•
Parse the source sentence
Transform the parse tree with transfer rules
Translate source words
Get the target sentence from the tree
Source parser
A translation lexicon
A set of transfer rules
An example: Mary bought a book yesterday.
Transfer-based MT (cont)
• Parsing: linguistically motivated grammar or formal
grammar?
• Transfer:
– context-free rules? A path on a dependency tree?
– Apply at most one rule at each level?
– How are rules created?
• Translating words: word-to-word translation?
• Generation: using LM or other additional knowledge?
• How to create the needed resources automatically?
Interlingua
• For n languages, we need n(n-1) MT systems.
• Interlingua uses a language-independent
representation.
• Conceptually, Interlingua is elegant: we only
need n analyzers, and n generators.
• Resource needed:
– A language-independent representation
– Sophisticated analyzers
– Sophisticated generators
Interlingua (cont)
• Questions:
– Does language-independent meaning representation
really exist? If so, what does it look like?
– It requires deep analysis: how to get such an
analyzer: e.g., semantic analysis
– It requires non-trivial generation: How is that done?
– It forces disambiguation at various levels: lexical,
syntactic, semantic, discourse levels.
– It cannot take advantage of similarities between a
particular language pair.
Example-based MT
• Basic idea: translate a sentence by using the
closest match in parallel data.
• First proposed by Nagao (1981).
• Ex:
– Training data:
• w1 w2 w3 w4  w1’ w2’ w3’ w4’
• w5 w6 w7  w5’ w6’ w7’
• w8 w9  w8’ w9’
– Test sent:
• w1 w2 w6 w7 w9  w1’ w2’ w6’ w7’ w9’
EMBT (cont)
• Types of EBMT:
– Lexical (shallow)
– Morphological / POS analysis
– Parse-tree based (deep)
• Types of data required by EBMT systems:
–
–
–
–
Parallel text
Bilingual dictionary
Thesaurus for computing semantic similarity
Syntactic parser, dependency parser, etc.
EBMT (cont)
• Word alignment: using dictionary and heuristics
 exact match
• Generalization:
– Clusters: dates, numbers, colors, shapes, etc.
– Clusters can be built by hand or learned automatically.
• Ex:
– Exact match: 12 players met in Paris last Tuesday 
12 Spieler trafen sich letzen Dienstag in Paris
– Templates: $num players met in $city $time 
$num Spieler trafen sich $time in $city
Statistical MT
• Basic idea: learn all the parameters from parallel data.
• Major types:
– Word-based
– Phrase-based
• Strengths:
– Easy to build, and it requires no human knowledge
– Good performance when a large amount of training data is
available.
• Weaknesses:
– How to express linguistic generalization?
Comparison of resource requirement
Transferbased
Interlingua
EBMT
dictionary
+
+
+
Transfer
rules
+
parser
+
+
+ (?)
semantic
analyzer
parallel data
others
SMT
+
+
Universal
thesaurus
representation
+
Hybrid MT
•
Basic idea: combine strengths of different approaches:
–
–
–
–
•
Syntax-based: generalization at syntactic level
Interlingua: conceptually elegant
EBMT: memorizing translation of n-grams; generalization at various level.
SMT: fully automatic; using LM; optimizing some objective functions.
Types of hybrid HT:
– Borrowing concepts/methods:
• SMT from EBMT: phrase-based SMT; Alignment templates
• EBMT from SMT: automatically learned translation lexicon
• Transfer-based from SMT: automatically learned translation lexicon, transfer rules;
using LM
• …
– Using two MTs in a pipeline:
• Using transfer-based MT as a preprocessor of SMT
– Using multiple MTs in parallel, then adding a re-ranker.
Evaluation of MT
Evaluation
• Unlike many NLP tasks (e.g., tagging, chunking, parsing,
IE, pronoun resolution), there is no single gold standard
for MT.
• Human evaluation: accuracy, fluency, …
– Problem: expensive, slow, subjective, non-reusable.
• Automatic measures:
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–
–
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Edit distance
Word error rate (WER), Position-independent WER (PER)
Simple string accuracy (SSA), Generation string accuracy (GSA)
BLEU
Edit distance
• The Edit distance (a.k.a. Levenshtein
distance) is defined as the minimal cost of
transforming str1 into str2, using three
operations (substitution, insertion,
deletion).
• Use DP and the complexity is O(m*n).
WER, PER, and SSA
• WER (word error rate) is edit distance, divided by |Ref|.
• PER (position-independent WER): same as WER but
disregards word ordering
• SSA (Simple string accuracy) = 1 - WER
• Previous example:
–
–
–
–
–
–
Sys: w1 w2 w3 w4
Ref: w1 w3 w2
Edit distance = 2
WER=2/3
PER=1/3
SSA=1/3
Generation string accuracy (GSA)
Move  Ins  Del  Sub
GSA  1 
| Re f |
Example:
Ref: w1 w2 w3 w4
Sys: w2 w3 w4 w1
Del=1, Ins=1  SSA=1/2
Move=1, Del=0, Ins=0  GSA=3/4
BLEU
• Proposal by Papineni et. al. (2002)
• Most widely used in MT community.
• BLEU is a weighted average of n-gram precision
(pn) between system output and all references,
multiplied by a brevity penalty (BP).
N
BLEU  BP *  p nwn
n 1
 BP * p1 * p 2 * ... p N
N
1
( when wn  )
N
N-gram precision
• N-gram precision: the percent of n-grams in
the system output that are correct.
• Clipping:
–
–
–
–
Sys: the the the the the the
Ref: the cat sat on the mat
Unigram precision:
Max_Ref_count: the max number of times a
ngram occurs in any single reference translation.
Count clip  min( count , Max _ Re f _ Count )
N-gram precision
pn 
  Count
SSys ngramS
clip
(ngram)
  Count (ngram)
SSys ngramS
i.e. the percent of n-grams in the system output
that are correct (after clipping).
Brevity Penalty
• For each sent si in system output, find closest matching
reference ri (in terms of length).
Let c   | si |, r   | ri |
i
 1
BP   1r / c
e
i
if c  r
otherwise
• Longer system output is already penalized by the n-gram
precision measure.
An example
• Sys: The cat was on the mat
• Ref1: The cat sat on a mat
• Ref2: There was a cat on the mat
• Assuming N=3
• p1=5/6, p2=3/5, p3=1/4, BP=1  BLEU=0.50
• What if N=4?
Summary
• Course overview
• Major challenges in MT
– Choose the right words (root form, inflection,
spontaneous words)
– Put them in right positions (word order, unique
constructions, divergences)
Summary (cont)
• Major approaches
–
–
–
–
–
Transfer-based MT
Interlingua
Example-based MT
Statistical MT
Hybrid MT
• Evaluation of MT systems
– Edit distance
– WER, PER, SSA, GSA
– BLEU
Additional slides
Translation divergences
(based on Bonnie Dorr’s work)
• Thematic divergence: I like Mary 
S: Marta me gusta a mi (‘Mary pleases me’)
• Promotional divergence: John usually goes home 
S: Juan suele ira casa (‘John tends to go home’)
• Demotional divergence: I like eating G: Ich esse gern
(“I eat likingly)
• Structural divergence: John entered the house 
S: Juan entro en la casa (‘John entered in the house’)
Translation divergences (cont)
• Conflational divergence: I stabbed John 
S: Yo le di punaladas a Juan (‘I gave knifewounds to John’)
• Categorial divergence: I am hungry 
G: Ich habe Hunger (‘I have hunger’)
• Lexical divergence: John broke into the room 
S: Juan forzo la entrada al cuarto (‘John forced
the entry to the room’)
Calculating edit distance
• D(0, 0) = 0
• D(i, 0) = delCost * i
• D(0, j) = insCost * j
• D(i+1, j+1) =
min( D(i,j) + sub,
D(i+1, j) + insCost,
D(i, j+1) + delCost)
sub = 0
= subCost
if str1[i+1]=str2[j+1]
otherwise
An example
• Sys: w1 w2 w3 w4
• Ref: w1 w3 w2
• All three costs are 1.
• Edit distance=2
w1
w3
w2
0
1
2
3
w1
1
0
1
2
w2
2
1
1
1
w3
3
2
1
2
w4
4
3
2
2