1. Machine Translation Overview
Alon Lavie
Language Technologies Institute
Carnegie Mellon University
LTI Immigration Course
August 24, 2006

2. Machine Translation: History
MT started in 1940’s, one of the first conceived application of computers
Promising “toy” demonstrations in the 1950’s, failed miserably to scale up to “real” systems
AIPAC Report: MT recognized as an extremely difficult, “AI-complete” problem in the early 1960’s
MT Revival started in earnest in 1980s (US, Japan)
Field dominated by rule-based approaches, requiring 100s of K-years of manual development
Economic incentive for developing MT systems for small number of language pairs (mostly European languages)
March 24, 2006
LTI IC 2006

3. Machine Translation: Where are we today?
Age of Internet and Globalization – great demand for MT:
Multiple official languages of UN, EU, Canada, etc.
Documentation dissemination for large manufacturers (Microsoft, IBM, Caterpillar)
Economic incentive is still primarily within a small number of language pairs
Some fairly good commercial products in the market for these language pairs
Primarily a product of rule-based systems after many years of development
Pervasive MT between most language pairs still non-existent and not on the immediate horizon
March 24, 2006
LTI IC 2006

4. Example of Current Best MT
PAHO’s Spanam system:
Mediante petición recibida por la Comisión Interamericana de Derechos Humanos (en adelante …) el 6 de octubre de 1997, el señor Lino César Oviedo (en adelante …) denunció que la República del Paraguay (en adelante …) violó en su perjuicio los derechos a las garantías judiciales … en su contra.
Through petition received by the `Inter-American Commission on Human Rights` (hereinafter …) on 6 October 1997, Mr. Linen César Oviedo (hereinafter “the petitioner”) denounced that the Republic of Paraguay (hereinafter …) violated to his detriment the rights to the judicial guarantees, to the political participation, to // equal protection and to the honor and dignity consecrated in articles 8, 23, 24 and 11, respectively, of the `American Convention on Human Rights` (hereinafter …”), as a consequence of judgments initiated against it.
March 24, 2006
LTI IC 2006

5. Core Challenges of MT Ambiguity: Amount of required knowledge:
Human languages are highly ambiguous, and differently in different languages
Ambiguity at all “levels”: lexical, syntactic, semantic, language-specific constructions and idioms
Amount of required knowledge:
At least several 100k words, at least as many phrases, plus syntactic knowledge (i.e. translation rules). How do you acquire and construct a knowledge base that big that is (even mostly) correct and consistent?
March 24, 2006
LTI IC 2006

6. How to Tackle the Core Challenges
Manual Labor: 1000s of person-years of human experts developing large word and phrase translation lexicons and translation rules.
Example: Systran’s RBMT systems.
Lots of Parallel Data: data-driven approaches for finding word and phrase correspondences automatically from large amounts of sentence-aligned parallel texts. Example: Statistical MT systems.
Learning Approaches: learn translation rules automatically from small amounts of human translated and word-aligned data. Example: AVENUE’s XFER approach.
Simplify the Problem: build systems that are limited-domain or constrained in other ways. Examples: CATALYST, NESPOLE!.
March 24, 2006
LTI IC 2006

7. State-of-the-Art in MT
What users want:
General purpose (any text)
High quality (human level)
Fully automatic (no user intervention)
We can meet any 2 of these 3 goals today, but not all three at once:
FA HQ: Knowledge-Based MT (KBMT)
FA GP: Corpus-Based (Example-Based) MT
GP HQ: Human-in-the-loop (efficiency tool)
March 24, 2006
LTI IC 2006

8. Types of MT Applications:
Assimilation: multiple source languages, uncontrolled style/topic. General purpose MT, no semantic analysis. (GP FA or GP HQ)
Dissemination: one source language, controlled style, single topic/domain. Special purpose MT, full semantic analysis. (FA HQ)
Communication: Lower quality may be okay, but system robustness, real-time required.
March 24, 2006
LTI IC 2006

9. Approaches to MT: Vaquois MT Triangle
Interlingua
Give-information+personal-data (name=alon_lavie)
Generation
Analysis
Transfer
[s [vp accusative_pronoun “chiamare” proper_name]]
[s [np [possessive_pronoun “name”]]
[vp “be” proper_name]]
Direct
Mi chiamo Alon Lavie
My name is Alon Lavie
March 24, 2006
LTI IC 2006

10. Analysis and Generation Main Steps
Morphological analysis (word-level) and POS tagging
Syntactic analysis and disambiguation (produce syntactic parse-tree)
Semantic analysis and disambiguation (produce symbolic frames or logical form representation)
Map to language-independent Interlingua
Generation:
Generate semantic representation in TL
Sentence Planning: generate syntactic structure and lexical selections for concepts
Surface-form realization: generate correct forms of words
March 24, 2006
LTI IC 2006

11. Direct Approaches No intermediate stage in the translation
First MT systems developed in the 1950’s-60’s (assembly code programs)
Morphology, bi-lingual dictionary lookup, local reordering rules
“Word-for-word, with some local word-order adjustments”
Modern Approaches: EBMT and SMT
March 24, 2006
LTI IC 2006

12. Statistical MT (SMT)
Proposed by IBM in early 1990s: a direct, purely statistical, model for MT
Statistical translation models are trained on a sentence-aligned parallel bilingual corpus
Train word-level alignment models
Extract phrase-to-phrase correspondences
Apply them at runtime on source input and “decode”
Attractive: completely automatic, no manual rules, much reduced manual labor
Main drawbacks:
Effective only with large volumes (several mega-words) of parallel text
Broad domain, but domain-sensitive
Still viable only for small number of language pairs!
Impressive progress in last 5 years
Large DARPA funding programs (TIDES, GALE)
Lots of research in this direction
GIZA++, Pharoah, CAIRO
March 24, 2006
LTI IC 2006

13. Matches to Source Found
EBMT Paradigm
New Sentence (Source)
Yesterday, 200 delegates met with President Clinton.
Matches to Source Found
Yesterday, 200 delegates met behind closed doors…
Difficulties with President Clinton…
Gestern trafen sich 200 Abgeordnete hinter verschlossenen…
Schwierigkeiten mit Praesident Clinton…
Alignment (Sub-sentential)
Yesterday, 200 delegates met behind closed doors…
Difficulties with President Clinton over…
Gestern trafen sich 200 Abgeordnete hinter verschlossenen…
Schwierigkeiten mit Praesident Clinton…
Translated Sentence (Target)
March 24, 2006
Gestern trafen sich 200 Abgeordnete mit Praesident Clinton.

14. Transfer Approaches Syntactic Transfer: Semantic Transfer:
Analyze SL input sentence to its syntactic structure (parse tree)
Transfer SL parse-tree to TL parse-tree (various formalisms for specifying mappings)
Generate TL sentence from the TL parse-tree
Semantic Transfer:
Analyze SL input to a language-specific semantic representation (i.e., Case Frames, Logical Form)
Transfer SL semantic representation to TL semantic representation
Generate syntactic structure and then surface sentence in the TL
March 24, 2006
LTI IC 2006

15. Transfer Approaches Main Advantages and Disadvantages:
Syntactic Transfer:
No need for semantic analysis and generation
Syntactic structures are general, not domain specific  Less domain dependent, can handle open domains
Requires word translation lexicon
Semantic Transfer:
Requires deeper analysis and generation, symbolic representation of concepts and predicates  difficult to construct for open or unlimited domains
Can better handle non-compositional meaning structures  can be more accurate
No word translation lexicon – generate in TL from symbolic concepts
March 24, 2006
LTI IC 2006

16. Knowledge-based Interlingual MT
The classic “deep” Artificial Intelligence approach:
Analyze the source language into a detailed symbolic representation of its meaning
Generate this meaning in the target language
“Interlingua”: one single meaning representation for all languages
Nice in theory, but extremely difficult in practice:
What kind of representation?
What is the appropriate level of detail to represent?
How to ensure that the interlingua is in fact universal?
“Demonstration” is now set of KANT interlinguas
Downloaded here from AMTA interlingua workshop webpage: clicking on sentences show the IL
Note use of phrasal lexical units!
Start with #4: “simple” example (with a 2-level genl. quantifier!)
1: rel. clause
3: conj “but”, coordination, another genl. quant.
5: implicit rel. clause, apposition
6: “latter”/”former”
7: appositive VP
8: quote marks
13/14: complex [numbers different on frontpage and files!]
21/22: “all” is (:OR SINGULAR PLURAL) [ambiguity packing!]
March 24, 2006
LTI IC 2006

17. Interlingua versus Transfer
With interlingua, need only N parsers/ generators instead of N2 transfer systems: L2 L2 L3 L1 L3 L1
interlingua L6 L4 L6 L4 L5 L5
March 24, 2006
LTI IC 2006

18. Multi-Engine MT Apply several MT engines to each input in parallel
Create a combined translation from the individual translations
Goal is to combine strengths, and avoid weaknesses.
Along all dimensions: domain limits, quality, development time/cost, run-time speed, etc.
Various approaches to the problem
March 24, 2006
LTI IC 2006

19. Speech-to-Speech MT Speech just makes MT (much) more difficult:
Spoken language is messier
False starts, filled pauses, repetitions, out-of-vocabulary words
Lack of punctuation and explicit sentence boundaries
Current Speech technology is far from perfect
Need for speech recognition and synthesis in foreign languages
Robustness: MT quality degradation should be proportional to SR quality
Tight Integration: rather than separate sequential tasks, can SR + MT be integrated in ways that improves end-to-end performance?
March 24, 2006
LTI IC 2006

20. Major Sources of Translation Problems
Lexical Differences:
Multiple possible translations for SL word, or difficulties expressing SL word meaning in a single TL word
Structural Differences:
Syntax of SL is different than syntax of the TL: word order, sentence and constituent structure
Differences in Mappings of Syntax to Semantics:
Meaning in TL is conveyed using a different syntactic structure than in the SL
Idioms and Constructions
March 24, 2006
LTI IC 2006

21. MT at the LTI
LTI originated as the Center for Machine Translation (CMT) in 1985
MT continues to be a prominent sub-discipline of research with the LTI
More MT faculty than any of the other areas
More MT faculty than anywhere else
Active research on all main approaches to MT: Interlingua, Transfer, EBMT, SMT
Leader in the area of speech-to-speech MT
Multi-Engine MT (MEMT)
MT Evaluation (METEOR, BLANC)
March 24, 2006
LTI IC 2006

22. KBMT: KANT, KANTOO, CATALYST
Deep knowledge-based framework, with symbolic interlingua as intermediate representation
Syntactic and semantic analysis into a unambiguous detailed symbolic representation of meaning using unification grammars and transformation mappers
Generation into the target language using unification grammars and transformation mappers
First large-scale multi-lingual interlingua-based MT system deployed commercially:
CATALYST at Caterpillar: high quality translation of documentation manuals for heavy equipment
Limited domains and controlled English input
Minor amounts of post-editing
Active follow-on projects
Contact Faculty: Eric Nyberg and Teruko Mitamura
March 24, 2006
LTI IC 2006

23. EBMT Developed originally for the PANGLOSS system in the early 1990s
Translation between English and Spanish
Generalized EBMT under development for the past several years
Used in a variety of projects in recent years
DARPA TIDES and GALE programs
DIPLOMAT and TONGUES
Active research work on improving alignment and indexing, decoding from a lattice
Contact Faculty: Ralf Brown and Jaime Carbonell
March 24, 2006
LTI IC 2006

24. Statistical MT
Word-to-word and phrase-to-phrase translation pairs are acquired automatically from data and assigned probabilities based on a statistical model
Extracted and trained from very large amounts of sentence-aligned parallel text
Word alignment algorithms
Phrase detection algorithms
Translation model probability estimation
Main approach pursued in CMU systems in the DARPA/TIDES program and now in GALE
Chinese-to-English and Arabic-to-English
Most active work is on phrase detection and on advanced decoding techniques
Contact Faculty: Stephan Vogel and Alex Waibel
March 24, 2006
LTI IC 2006

25. Speech-to-Speech MT
Evolution from JANUS/C-STAR systems to NESPOLE!, LingWear, BABYLON, TC-STAR
Early 1990s: first prototype system that fully performed sp-to-sp (very limited domains)
Interlingua-based, but with shallow task-oriented representations:
“we have single and double rooms available”
[give-information+availability]
(room-type={single, double})
Semantic Grammars for analysis and generation
Multiple languages: English, German, French, Italian, Japanese, Korean, and others
Stat-MT applied in Speech-to-Speech scenarios
Most active work on portable speech translation on small devices: Arabic/English and Thai/English
Contact Faculty: Alan Black, Stephan Vogel, Tanja Schultz and Alex Waibel
March 24, 2006
LTI IC 2006

26. AVENUE/LETRAS: Learning-based Transfer MT
Develop new approaches for automatically acquiring syntactic MT transfer rules from small amounts of elicited translated and word-aligned data
Specifically designed to bootstrap MT for languages for which only limited amounts of electronic resources are available (particularly indigenous minority languages)
Use machine learning techniques to generalize transfer rules from specific translated examples
Combine with SMT-inspired decoding techniques for producing the best translation of new input from a lattice of translation segments
Languages: Hebrew, Hindi, Mapudungun, Quechua
Most active work on designing a typologically comprehensive elicitation corpus, advanced techniques for automatic rule learning, improved decoding, and rule refinement via user interaction
Contact Faculty: Alon Lavie, Lori Levin, Jaime Carbonell and Bob Frederking
March 24, 2006
LTI IC 2006

27. Multi-Engine MT
New approach developed over past two years under DoD and DARPA funding (used in GALE)
Main ideas:
Treat original engines as “black boxes”
Align the word and phrase correspondences between the translations
Build a collection of synthetic combinations based on the aligned words and phrases
Score the synthetic combinations based on Language Model and confidence measures
Select the top-scoring synthetic combination
Architecture Issues: integrating “workflows” that produce multiple translations and then combine them with MEMT
IBM’s UIMA architecture
Contact Faculty: Alon Lavie
March 24, 2006
LTI IC 2006

28. Synthetic Combination MEMT
Two Stage Approach:
Align: Identify common words and phrases across the translations provided by the engines
Decode: search the space of synthetic combinations of words/phrases and select the highest scoring combined translation
Example:
announced afghan authorities on saturday reconstituted four intergovernmental committees
The Afghan authorities on Saturday the formation of the four committees of government
March 24, 2006
LTI IC 2006

29. Synthetic Combination MEMT
Two Stage Approach:
Align: Identify common words and phrases across the translations provided by the engines
Decode: search the space of synthetic combinations of words/phrases and select the highest scoring combined translation
Example:
announced afghan authorities on saturday reconstituted four intergovernmental committees
The Afghan authorities on Saturday the formation of the four committees of government
MEMT: the afghan authorities announced on Saturday the formation of four intergovernmental committees
March 24, 2006
LTI IC 2006

30. Automatic MT Evaluation
METEOR: new metric developed at CMU
Improves upon BLEU metric developed by IBM and used extensively in recent years
Main ideas:
Assess the similarity between a machine-produced translation and (several) human reference translations
Similarity is based on word-to-word matching that matches:
Identical words
Morphological variants of same word (stemming)
synonyms
Similarity is based on weighted combination of Precision and Recall
Address fluency/grammaticality via a direct penalty: how well-ordered is the matching of the MT output with the reference?
Improved levels of correlation with human judgments of MT Quality
Contact Faculty: Alon Lavie
March 24, 2006
LTI IC 2006

31. The METEOR Metric Example: P = 5/8 =0.625 R = 5/14 = 0.357
Reference: “the Iraqi weapons are to be handed over to the army within two weeks”
MT output: “in two weeks Iraq’s weapons will give army”
Matching: Ref: Iraqi weapons army two weeks
MT: two weeks Iraq’s weapons army
P = 5/8 = R = 5/14 = 0.357
Fmean = 10*P*R/(9P+R) =
Fragmentation: 3 frags of 5 words = (3-1)/(5-1) = 0.50
Discounting factor: DF = 0.5 * (frag**3) =
Final score: Fmean * (1- DF) = * =
March 24, 2006
LTI IC 2006

32. Summary
Main challenges for current state-of-the-art MT approaches - Coverage and Accuracy:
Acquiring broad-coverage high-accuracy translation lexicons (for words and phrases)
learning syntactic mappings between languages from parallel word-aligned data
overcoming syntax-to-semantics differences and dealing with constructions
Stronger Target Language Modeling
March 24, 2006
LTI IC 2006

33. Questions…
March 24, 2006
LTI IC 2006

34. Example
Sys1: feature prominently venezuela ranked fifth in exporting oil field in the world and eighth in production
Sys2: Venezuela is occupied by the fifth place to export oil in the world, eighth in production
Sys3: Venezuela the top ranked fifth in the oil export in the world and the eighth in the production
MEMT Sentence :
Selected : venezuela is the top ranked fifth in the oil export in the world to eighth in production.
March 24, 2006
LTI IC 2006

35. MEMT Example
IBM: korea stands ready to allow visits to verify that it does not manufacture nuclear weapons
ISI: North Korea Is Prepared to Allow Washington to Verify that It Does Not Make Nuclear Weapons
CMU: North Korea prepared to allow Washington to the verification of that is to manufacture nuclear weapons
Selected MEMT Sentence :
north korea is prepared to allow washington to verify that it does not manufacture nuclear weapons ( )
March 24, 2006
LTI IC 2006

36. Example
Sys1: announced afghan authorities on Saturday reconstituted four intergovernmental committees accelerate the process of disarmament removal packing between fighters and pictures of war are still have enjoyed substantial influence
Sys2: The Afghan authorities on Saturday the formation of the four committees of government to speed up the process of disarmament demobilization of fighters of the leaders of the war who still have a significant influence.
Sys3: the authorities announced Saturday Afghan form four committees government accelerate the process of disarmament and complete disarmament and demobilization followed the leaders of the war who continues to enjoy considerable influence
MEMT Sentence :
Selected : the afghan authorities on Saturday announced the formation of the four committees of government to speed up the process of disarmament and demobilization of fighters of the leaders of the war who still have a significant influence.
March 24, 2006
LTI IC 2006

37. MEMT Example
IBM: the sri lankan prime minister criticizes head of the country's :
ISI: The President of the Sri Lankan Prime Minister Criticized the President of the Country :
CMU: Lankan Prime Minister criticizes her country:
MEMT Sentence :
Selected: the sri lankan prime minister criticizes president of the country
March 24, 2006
LTI IC 2006

38. Example
Sys1: victims russians are one man and his wife and abusing their eight year old daughter plus a ( 11 and 7 years ) man and his wife and driver , egyptian nationality . :
Sys2: The victims were Russian man and his wife, daughter of the most from the age of eight years in addition to the young girls ) 11 7 years ( and a man and his wife and the bus driver Egyptian nationality. :
Sys3: the victims Cruz man who wife and daughter both critical of the eight years old addition to two Orient ( 11 ) 7 years ) woman , wife of bus drivers Egyptian nationality . :
MEMT Sentence :
Selected : the victims were russian man and his wife and daughter of the eight years from the age of a 11 and 7 years in addition to man and his wife and bus drivers egyptian nationality
Oracle : the victims were russian man and wife and his daughter of the eight years old from the age of a 11 and 7 years in addition to the man and his wife and bus drivers egyptian nationality young girls
March 24, 2006
LTI IC 2006

39. Lexical Differences
SL word has several different meanings, that translate differently into TL
Ex: financial bank vs. river bank
Lexical Gaps: SL word reflects a unique meaning that cannot be expressed by a single word in TL
Ex: English snub doesn’t have a corresponding verb in French or German
TL has finer distinctions than SL  SL word should be translated differently in different contexts
Ex: English wall can be German wand (internal), mauer (external)
March 24, 2006
LTI IC 2006

40. Lexical Differences Lexical gaps:
Examples: these have no direct equivalent in English: gratiner (v., French, “to cook with a cheese coating”) ōtosanrin (n., Japanese, “three-wheeled truck or van”)
March 24, 2006
LTI IC 2006

41. Lexical Differences [From Hutchins & Somers] March 24, 2006
LTI IC 2006

42. MT Handling of Lexical Differences
Direct MT and Syntactic Transfer:
Lexical Transfer stage uses bilingual lexicon
SL word can have multiple translation entries, possibly augmented with disambiguation features or probabilities
Lexical Transfer can involve use of limited context (on SL side, TL side, or both)
Lexical Gaps can partly be addressed via phrasal lexicons
Semantic Transfer:
Ambiguity of SL word must be resolved during analysis  correct symbolic representation at semantic level
TL Generation must select appropriate word or structure for correctly conveying the concept in TL
March 24, 2006
LTI IC 2006

43. Structural Differences
Syntax of SL is different than syntax of the TL:
Word order within constituents:
English NPs: art adj n the big boy
Hebrew NPs: art n art adj ha yeled ha gadol
Constituent structure:
English is SVO: Subj Verb Obj I saw the man
Modern Arabic is VSO: Verb Subj Obj
Different verb syntax:
Verb complexes in English vs. in German
I can eat the apple Ich kann den apfel essen
Case marking and free constituent order
German and other languages that mark case:
den apfel esse Ich the(acc) apple eat I(nom)
March 24, 2006
LTI IC 2006

44. MT Handling of Structural Differences
Direct MT Approaches:
No explicit treatment: Phrasal Lexicons and sentence level matches or templates
Syntactic Transfer:
Structural Transfer Grammars
Trigger rule by matching against syntactic structure on SL side
Rule specifies how to reorder and re-structure the syntactic constituents to reflect syntax of TL side
Semantic Transfer:
SL Semantic Representation abstracts away from SL syntax to functional roles  done during analysis
TL Generation maps semantic structures to correct TL syntax
March 24, 2006
LTI IC 2006

45. Syntax-to-Semantics Differences
Meaning in TL is conveyed using a different syntactic structure than in the SL
Changes in verb and its arguments
Passive constructions
Motion verbs and state verbs
Case creation and case absorption
Main Distinction from Structural Differences:
Structural differences are mostly independent of lexical choices and their semantic meaning  addressed by transfer rules that are syntactic in nature
Syntax-to-semantic mapping differences are meaning-specific: require the presence of specific words (and meanings) in the SL
March 24, 2006
LTI IC 2006

46. Syntax-to-Semantics Differences
Structure-change example:
I like swimming
“Ich scwhimme gern”
I swim gladly
March 24, 2006
LTI IC 2006

47. Syntax-to-Semantics Differences
Verb-argument example:
Jones likes the film.
“Le film plait à Jones.”
(lit: “the film pleases to Jones”)
Use of case roles can eliminate the need for this type of transfer
Jones = Experiencer
film = Theme
March 24, 2006
LTI IC 2006

48. Syntax-to-Semantics Differences
Passive Constructions
Example: French reflexive passives: Ces livres se lisent facilement *”These books read themselves easily” These books are easily read
March 24, 2006
LTI IC 2006

49. Same intention, different syntax
rigly bitiwgacny
my leg hurts
candy wagac fE rigly
I have pain in my leg
rigly bitiClimny
fE wagac fE rigly
there is pain in my leg
rigly bitinqaH calya
my leg bothers on me
Romanization of Arabic from CallHome Egypt.
March 24, 2006
LTI IC 2006

50. MT Handling of Syntax-to-Semantics Differences
Direct MT Approaches:
No Explicit treatment: Phrasal Lexicons and sentence level matches or templates
Syntactic Transfer:
“Lexicalized” Structural Transfer Grammars
Trigger rule by matching against “lexicalized” syntactic structure on SL side: lexical and functional features
Rule specifies how to reorder and re-structure the syntactic constituents to reflect syntax of TL side
Semantic Transfer:
SL Semantic Representation abstracts away from SL syntax to functional roles  done during analysis
TL Generation maps semantic structures to correct TL syntax
March 24, 2006
LTI IC 2006

51. Example of Structural Transfer Rule (verb-argument)
[From Hutchins & Somers]
March 24, 2006
LTI IC 2006

52. Semantic Transfer: Theta Structure (case roles)
[From Hutchins & Somers]
Abstracts away from grammatical functions
Looks more like a “semantic f-structure”
The basis for “semantic transfer”
March 24, 2006
LTI IC 2006

53. Idioms and Constructions
Main Distinction: meaning of whole is not directly compositional from meaning of its sub-parts  no compositional translation
Examples:
George is a bull in a china shop
He kicked the bucket
Can you please open the window?
March 24, 2006
LTI IC 2006

54. Formulaic Utterances Good night. tisbaH cala xEr waking up on good
Romanization of Arabic from CallHome Egypt
March 24, 2006
LTI IC 2006

55. Constructions
Identifying speaker intention rather than literal meaning for formulaic and task-oriented sentences.
How about … suggestion
Why don’t you… suggestion
Could you tell me… request info.
I was wondering… request info.
March 24, 2006
LTI IC 2006

56. MT Handling of Constructions and Idioms
Direct MT Approaches:
No Explicit treatment: Phrasal Lexicons and sentence level matches or templates
Syntactic Transfer:
No effective treatment
“Highly Lexicalized” Structural Transfer rules can handle some constructions
Trigger rule by matching against entire construction, including structure on SL side
Rule specifies how to generate the correct construction on the TL side
Semantic Transfer:
Analysis must capture non-compositional representation of the idiom or construction  specialized rules
TL Generation maps construction semantic structures to correct TL syntax and lexical words
March 24, 2006
LTI IC 2006

57. Transfer with Strong Decoding
Learning Module
Transfer Rules
{PP,4894} ;;Score: PP::PP [NP POSTP] -> [PREP NP] ((X2::Y1) (X1::Y2))
Translation Lexicon
Run Time Transfer System
Lattice
Decoder
English Language Model
Word-to-Word Translation Probabilities
Word-aligned elicited data
March 24, 2006
LTI IC 2006

58. MT for Minority and Indigenous Languages: Challenges
Minimal amount of parallel text
Possibly competing standards for orthography/spelling
Often relatively few trained linguists
Access to native informants possible
Need to minimize development time and cost
March 24, 2006
LTI IC 2006

59. Learning Transfer-Rules for Languages with Limited Resources
Rationale:
Large bilingual corpora not available
Bilingual native informant(s) can translate and align a small pre-designed elicitation corpus, using elicitation tool
Elicitation corpus designed to be typologically comprehensive and compositional
Transfer-rule engine and new learning approach support acquisition of generalized transfer-rules from the data
March 24, 2006
LTI IC 2006

60. English-Hindi Example
March 24, 2006
LTI IC 2006

61. GEBMT vs. Statistical MT
Generalized-EBMT (GEBMT) uses examples at run time, rather than training a parameterized model. Thus:
GEBMT can work with a smaller parallel corpus than Stat MT
Large target language corpus still useful for generating target language model
Much faster to “train” (index examples) than Stat MT; until recently was much faster at run time as well
Generalizes in a different way than Stat MT (whether this is better or worse depends on match between Statistical model and reality):
Stat MT can fail on a training sentence, while GEBMT never will
GEBMT generalizations based on linguistic knowledge, rather than statistical model design
March 24, 2006
LTI IC 2006

62. MEMT chart example Russian leaders signed KBMT (0.8) compact of peace
EBMT (0.65)
political leaders
EBMT (0.9)
compact of
EBMT (0.7)
civilian
GLOSS (1.0)
tactful
DICT (1.0)
pact
of peace
EBMT (1.0)
civil
expedients
bargain
for
civil peace
political
Russians
subscribe of
quiet
leaders
politic
Russian
sign
compact
peace
lideres
politicos
rusos
firman
pacto de
paz
Point out overlapping edges
March 24, 2006
LTI IC 2006

63. Why Machine Translation for Minority and Indigenous Languages?
Commercial MT economically feasible for only a handful of major languages with large resources (corpora, human developers)
Is there hope for MT for languages with limited resources?
Benefits include:
Better government access to indigenous communities (Epidemics, crop failures, etc.)
Better indigenous communities participation in information-rich activities (health care, education, government) without giving up their languages.
Language preservation
Civilian and military applications (disaster relief)
March 24, 2006
LTI IC 2006

64. English-Chinese Example
March 24, 2006
LTI IC 2006

65. Spanish-Mapudungun Example
March 24, 2006
LTI IC 2006

66. English-Arabic Example
March 24, 2006
LTI IC 2006

67. The Elicitation Corpus
Translated, aligned by bilingual informant
Corpus consists of linguistically diverse constructions
Based on elicitation and documentation work of field linguists (e.g. Comrie 1977, Bouquiaux 1992)
Organized compositionally: elicit simple structures first, then use them as building blocks
Goal: minimize size, maximize linguistic coverage
March 24, 2006
LTI IC 2006

68. Transfer Rule Formalism
;SL: the old man, TL: ha-ish ha-zaqen
NP::NP [DET ADJ N] -> [DET N DET ADJ] (
(X1::Y1)
(X1::Y3)
(X2::Y4)
(X3::Y2)
((X1 AGR) = *3-SING)
((X1 DEF = *DEF)
((X3 AGR) = *3-SING)
((X3 COUNT) = +)
((Y1 DEF) = *DEF)
((Y3 DEF) = *DEF)
((Y2 AGR) = *3-SING)
((Y2 GENDER) = (Y4 GENDER)) )
Type information
Part-of-speech/constituent information
Alignments
x-side constraints
y-side constraints
xy-constraints,
e.g. ((Y1 AGR) = (X1 AGR))
March 24, 2006
LTI IC 2006

69. Transfer Rule Formalism (II)
;SL: the old man, TL: ha-ish ha-zaqen
NP::NP [DET ADJ N] -> [DET N DET ADJ] (
(X1::Y1)
(X1::Y3)
(X2::Y4)
(X3::Y2)
((X1 AGR) = *3-SING)
((X1 DEF = *DEF)
((X3 AGR) = *3-SING)
((X3 COUNT) = +)
((Y1 DEF) = *DEF)
((Y3 DEF) = *DEF)
((Y2 AGR) = *3-SING)
((Y2 GENDER) = (Y4 GENDER)) )
Value constraints
Agreement constraints
March 24, 2006
LTI IC 2006

70. The Transfer Engine Analysis Transfer Generation
Source text is parsed into its grammatical structure. Determines transfer application ordering.
Example:
他 看 书。(he read book) S
NP VP
N V NP
他 看 书
Transfer
A target language tree is created by reordering, insertion, and deletion.
he read DET N
a book
Article “a” is inserted into object NP. Source words translated with transfer lexicon.
Generation
Target language constraints are checked and final translation produced.
E.g. “reads” is chosen over “read” to agree with “he”.
Final translation:
“He reads a book”
March 24, 2006
LTI IC 2006

71. Rule Learning - Overview
Goal: Acquire Syntactic Transfer Rules
Use available knowledge from the source side (grammatical structure)
Three steps:
Flat Seed Generation: first guesses at transfer rules; flat syntactic structure
Compositionality: use previously learned rules to add hierarchical structure
Seeded Version Space Learning: refine rules by learning appropriate feature constraints
March 24, 2006
LTI IC 2006

72. Flat Seed Rule Generation
Learning Example: NP
Eng: the big apple
Heb: ha-tapuax ha-gadol
Generated Seed Rule:
NP::NP [ART ADJ N]  [ART N ART ADJ]
((X1::Y1)
(X1::Y3)
(X2::Y4)
(X3::Y2))
March 24, 2006
LTI IC 2006

73. Flat Seed Generation
Create a transfer rule that is specific to the sentence pair, but abstracted to the POS level. No syntactic structure.
Element
Source
SL POS sequence
f-structure
TL POS sequence
TL dictionary, aligned SL words
Type information
corpus, same on SL and TL
Alignments
informant
x-side constraints
y-side constraints
TL dictionary, aligned SL words (list of projecting features)
March 24, 2006
LTI IC 2006

74. Compositionality Initial Flat Rules:
S::S [ART ADJ N V ART N]  [ART N ART ADJ V P ART N]
((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2) (X4::Y5) (X5::Y7) (X6::Y8))
NP::NP [ART ADJ N]  [ART N ART ADJ]
((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2))
NP::NP [ART N]  [ART N]
((X1::Y1) (X2::Y2))
Generated Compositional Rule:
S::S [NP V NP]  [NP V P NP]
((X1::Y1) (X2::Y2) (X3::Y4))
March 24, 2006
LTI IC 2006

75. Compositionality - Overview
Traverse the c-structure of the English sentence, add compositional structure for translatable chunks
Adjust constituent sequences, alignments
Remove unnecessary constraints, i.e. those that are contained in the lower-level rule
March 24, 2006
LTI IC 2006

76. Seeded Version Space Learning
Input: Rules and their Example Sets
S::S [NP V NP]  [NP V P NP] {ex1,ex12,ex17,ex26}
((X1::Y1) (X2::Y2) (X3::Y4))
NP::NP [ART ADJ N]  [ART N ART ADJ] {ex2,ex3,ex13}
((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2))
NP::NP [ART N]  [ART N] {ex4,ex5,ex6,ex8,ex10,ex11}
((X1::Y1) (X2::Y2))
Output: Rules with Feature Constraints:
S::S [NP V NP]  [NP V P NP]
((X1::Y1) (X2::Y2) (X3::Y4)
(X1 NUM = X2 NUM)
(Y1 NUM = Y2 NUM)
(X1 NUM = Y1 NUM))
March 24, 2006
LTI IC 2006

77. Seeded Version Space Learning: Overview
Goal: add appropriate feature constraints to the acquired rules
Methodology:
Preserve general structural transfer
Learn specific feature constraints from example set
Seed rules are grouped into clusters of similar transfer structure (type, constituent sequences, alignments)
Each cluster forms a version space: a partially ordered hypothesis space with a specific and a general boundary
The seed rules in a group form the specific boundary of a version space
The general boundary is the (implicit) transfer rule with the same type, constituent sequences, and alignments, but no feature constraints
March 24, 2006
LTI IC 2006

78. Seeded Version Space Learning: Generalization
The partial order of the version space:
Definition: A transfer rule tr1 is strictly more general than another transfer rule tr2 if all f-structures that are satisfied by tr2 are also satisfied by tr1.
Generalize rules by merging them:
Deletion of constraint
Raising two value constraints to an agreement constraint, e.g.
((x1 num) = *pl), ((x3 num) = *pl) 
((x1 num) = (x3 num))
March 24, 2006
LTI IC 2006

79. Seeded Version Space Learning
NP v det n NP VP …
Group seed rules into version spaces as above.
Make use of partial order of rules in version space. Partial order is defined
via the f-structures satisfying the constraints.
Generalize in the space by repeated merging of rules:
Deletion of constraint
Moving value constraints to agreement constraints, e.g.
((x1 num) = *pl), ((x3 num) = *pl) 
((x1 num) = (x3 num)
4. Check translation power of generalized rules against sentence pairs
March 24, 2006
LTI IC 2006

80. Seeded Version Space Learning: The Search
The Seeded Version Space algorithm itself is the repeated generalization of rules by merging
A merge is successful if the set of sentences that can correctly be translated with the merged rule is a superset of the union of sets that can be translated with the unmerged rules, i.e. check power of rule
Merge until no more successful merges
March 24, 2006
LTI IC 2006

81. Seeded VSL: Some Open Issues
Three types of constraints:
X-side constrain applicability of rule
Y-side assist in generation
X-Y transfer features from SL to TL
Which of the three types improves translation performance?
Use rules without features to populate lattice, decoder will select the best translation…
Learn only X-Y constraints, based on list of universal projecting features
Other notions of version-spaces of feature constraints:
Current feature learning is specific to rules that have identical transfer components
Important issue during transfer is to disambiguate among rules that have same SL side but different TL side – can we learn effective constraints for this?
March 24, 2006
LTI IC 2006

82. Examples of Learned Rules (Hindi-to-English)
{NP,14244}
;;Score:0.0429
NP::NP [N] -> [DET N] (
(X1::Y2) )
{NP,14434}
;;Score:0.0040
NP::NP [ADJ CONJ ADJ N] ->
[ADJ CONJ ADJ N]
(X1::Y1) (X2::Y2)
(X3::Y3) (X4::Y4)
{PP,4894} ;;Score: PP::PP [NP POSTP] -> [PREP NP] ( (X2::Y1) (X1::Y2) )
March 24, 2006
LTI IC 2006

83. Manual Transfer Rules: Hindi Example
;; PASSIVE OF SIMPLE PAST (NO AUX) WITH LIGHT VERB
;; passive of 43 (7b)
{VP,28}
VP::VP : [V V V] -> [Aux V] (
(X1::Y2)
((x1 form) = root)
((x2 type) =c light)
((x2 form) = part)
((x2 aspect) = perf)
((x3 lexwx) = 'jAnA')
((x3 form) = part)
((x3 aspect) = perf)
(x0 = x1)
((y1 lex) = be)
((y1 tense) = past)
((y1 agr num) = (x3 agr num))
((y1 agr pers) = (x3 agr pers))
((y2 form) = part) )
March 24, 2006
LTI IC 2006

84. Manual Transfer Rules: ExampleNP
PP NP1
NP P Adj N
N1 ke eka aXyAya N
jIvana NP
NP PP
Adj N P NP
one chapter of N1 N
life
; NP1 ke NP2 -> NP2 of NP1
; Ex: jIvana ke eka aXyAya
; life of (one) chapter
; ==> a chapter of life ;
{NP,12}
NP::NP : [PP NP1] -> [NP1 PP] (
(X1::Y2)
(X2::Y1)
; ((x2 lexwx) = 'kA') )
{NP,13}
NP::NP : [NP1] -> [NP1]
(X1::Y1)
{PP,12}
PP::PP : [NP Postp] -> [Prep NP]
March 24, 2006
LTI IC 2006

85. A Limited Data Scenario for Hindi-to-English
Conducted during a DARPA “Surprise Language Exercise” (SLE) in June 2003
Put together a scenario with “miserly” data resources:
Elicited Data corpus: phrases
Cleaned portion (top 12%) of LDC dictionary: ~2725 Hindi words (23612 translation pairs)
Manually acquired resources during the SLE:
500 manual bigram translations
72 manually written phrase transfer rules
105 manually written postposition rules
48 manually written time expression rules
No additional parallel text!!
March 24, 2006
LTI IC 2006

86. Manual Grammar Development
Covers mostly NPs, PPs and VPs (verb complexes)
~70 grammar rules, covering basic and recursive NPs and PPs, verb complexes of main tenses in Hindi (developed in two weeks)
March 24, 2006
LTI IC 2006

87. Adding a “Strong” Decoder
XFER system produces a full lattice of translation fragments, ranging from single words to long phrases or sentences
Edges are scored using word-to-word translation probabilities, trained from the limited bilingual data
Decoder uses an English LM (70m words)
Decoder can also reorder words or phrases (up to 4 positions ahead)
For XFER(strong) , ONLY edges from basic XFER system are used!
March 24, 2006
LTI IC 2006

88. Testing Conditions
Tested on section of JHU provided data: 258 sentences with four reference translations
SMT system (stand-alone)
EBMT system (stand-alone)
XFER system (naïve decoding)
XFER system with “strong” decoder
No grammar rules (baseline)
Manually developed grammar rules
Automatically learned grammar rules
XFER+SMT with strong decoder (MEMT)
March 24, 2006
LTI IC 2006

89. Automatic MT Evaluation Metrics
Intends to replace or complement human assessment of translation quality of MT produced translation
Principle idea: compare how similar is the MT produced translation with human translation(s) of the same input
Main metric in use today: IBM’s BLEU
Count n-gram (unigrams, bigrams, trigrams, etc) overlap between the MT output and several reference translations
Calculate a combined n-gram precision score
NIST variant of BLEU used for official DARPA evaluations
March 24, 2006
LTI IC 2006

90. Results on JHU Test Set System BLEU M-BLEU NIST EBMT 0.058 0.165 4.22
SMT
0.093
0.191
4.64
XFER (naïve) man grammar
0.055
0.177
4.46
XFER (strong) no grammar
0.109
0.224
5.29
XFER (strong) learned grammar
0.116
0.231
5.37
XFER (strong) man grammar
0.135
0.243
5.59
XFER+SMT
0.136
5.65
March 24, 2006
LTI IC 2006

91. Effect of Reordering in the Decoder
March 24, 2006
LTI IC 2006

92. Observations and Lessons (I)
XFER with strong decoder outperformed SMT even without any grammar rules in the miserly data scenario
SMT Trained on elicited phrases that are very short
SMT has insufficient data to train more discriminative translation probabilities
XFER takes advantage of Morphology
Token coverage without morphology:
Token coverage with morphology:
Manual grammar currently somewhat better than automatically learned grammar
Learned rules did not yet use version-space learning
Large room for improvement on learning rules
Importance of effective well-founded scoring of learned rules
March 24, 2006
LTI IC 2006

93. Observations and Lessons (II)
MEMT (XFER and SMT) based on strong decoder produced best results in the miserly scenario.
Reordering within the decoder provided very significant score improvements
Much room for more sophisticated grammar rules
Strong decoder can carry some of the reordering “burden”
March 24, 2006
LTI IC 2006

94. XFER MT for Hebrew-to-English
Two month intensive effort to apply our XFER approach to the development of a Hebrew-to-English MT system
Challenges:
No large parallel corpus
Only limited coverage translation lexicon
Morphology: incomplete analyzer available
Plan:
Collect available resources, establish methodology for processing Hebrew input
Translate and align Elicitation Corpus
Learn XFER rules
Develop (small) manual XFER grammar as a point of comparison
Evaluate performance on unseen test data using automatic evaluation metrics
March 24, 2006
LTI IC 2006

95. Hebrew-to-English XFER System
First end-to-end integration of system completed yesterday (March 2nd)
No transfer rules yet, just word-to-word Hebrew-to-English translation
No strong decoding yet
Amusing Example:
office brains the government crack H$BW& in committee
the elections the central et the possibility conduct poll
crowd about TWKNIT the NSIGH from goat
March 24, 2006
LTI IC 2006

96. Conclusions
Transfer rules (both manual and learned) offer significant contributions that can complement existing data-driven approaches
Also in medium and large data settings?
Initial steps to development of a statistically grounded transfer-based MT system with:
Rules that are scored based on a well-founded probability model
Strong and effective decoding that incorporates the most advanced techniques used in SMT decoding
Working from the “opposite” end of research on incorporating models of syntax into “standard” SMT systems [Knight et al]
Our direction makes sense in the limited data scenario
March 24, 2006
LTI IC 2006

97. Future Directions
Continued work on automatic rule learning (especially Seeded Version Space Learning)
Improved leveraging from manual grammar resources, interaction with bilingual speakers
Developing a well-founded model for assigning scores (probabilities) to transfer rules
Improving the strong decoder to better fit the specific characteristics of the XFER model
MEMT with improved
Combination of output from different translation engines with different scorings
strong decoding capabilities
March 24, 2006
LTI IC 2006

98. Language Modeling for MT
Technique stolen from Speech Recognition
Try to match the statistics of English
Trigram example: “George W. …”
Combine quality score with trigram score, to factor in “English-like-ness”
Problem: this gives billions of possible overall translations
Solution: “beam search”. At each step, throw out all but the “best” possibilities
March 24, 2006
LTI IC 2006

99. Speech-to-speech translation for eCommerce
CMU, Karlsruhe, IRST, CLIPS, 2 commercial partners
Improved limited-domain speech translation
Experiment with multimodality and with MEMT
EU-side has strict scheduling and deliverables
First test domain: Italian travel agency
Second “showcase”: international Help desk
Tied in to CSTAR-III
March 24, 2006
LTI IC 2006