Machine Translation Overview

1
Machine Translation Overview

• Alon Lavie
• Language Technologies Institute
• Carnegie Mellon University
• Open House
• March 18, 2005

2
Machine Translation History

• MT started in 1940s, one of the first conceived
  application of computers
• Promising toy demonstrations in the 1950s,
  failed miserably to scale up to real systems
• AIPAC Report MT recognized as an extremely
  difficult, AI-complete problem in the early
  1960s
• 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)

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

4
Best Current General-purpose MT

• PAHOs 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.

5
Core Challenges of MT

• Ambiguity
• 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, about 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?

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 Systrans 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
  AVENUEs XFER approach
• Simplify the Problem build systems that are
  limited-domain or constrained in other ways.
  Examples CATALYST, NESPOLE!

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)

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
  degraded input, real-time required.

9
Approaches to MT Vaquois MT Triangle
Interlingua
Give-informationpersonal-data (namealon_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
10
Knowledge-based Interlingual MT

• The obvious 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

11
The Interlingua KBMT approach

• With interlingua, need only N parsers/ generators
  instead of N2 transfer systems

L2
L2
L3
L1
L1
L3
interlingua
L6
L4
L6
L4
L5
L5
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 translation corpus
• Attractive completely automatic, no manual
  rules, much reduced manual labor
• Main drawbacks
• Effective only with huge volumes (several
  mega-words) of parallel text
• Very domain-sensitive
• Still viable only for small number of language
  pairs!
• Impressive progress in last 3-4 years due to
  large DARPA funding program (TIDES)

13
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)
Gestern trafen sich 200 Abgeordnete mit
Praesident Clinton.
14
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

15
Multi-Engine MT

• Apply several MT engines to each input use
  statistical language modeller to select best
  combination of outputs.
• Goal is to combine strengths, and avoid
  weaknesses.
• Along all dimensions domain limits, quality,
  development time/cost, run-time speed, etc.
• Used in various projects

16
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?

17
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

18
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

19
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
• Currently one of the two MT approaches developed
  at CMU for the DARPA/TIDES program
• Chinese-to-English, large and very large amounts
  of sentence-aligned parallel data
• Active research work on improving alignment and
  indexing, decoding from a lattice
• Contact Faculty Ralf Brown and Jaime Carbonell

20
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
• Chinese-to-English and Arabic-to-English
• Most active work is on phrase detection and on
  advanced lattice decoding
• Contact Faculty Stephan Vogel and Alex Waibel

21
Speech-to-Speech MT

• Evolution from JANUS/C-STAR systems to NESPOLE!,
  LingWear, BABYLON
• Early 1990s first prototype system that fully
  performed sp-to-sp (very limited domain)
• Interlingua-based, but with shallow task-oriented
  representations
• we have single and double rooms available
• give-informationavailability
• (room-typesingle, double)
• Semantic Grammars for analysis and generation
• Multiple languages English, German, French,
  Italian, Japanese, Korean, and others
• Most active work on portable speech translation
  on small devices Arabic/English and Thai/English
• Contact Faculty Alan Black, Tanja Schultz and
  Alex Waibel (also Alon Lavie and Lori Levin)

22
AVENUE Transfer-based 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 decoding techniques from SMT 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 and
  Jaime Carbonell

23
Transfer with Strong Decoding
24
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

25
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

26
English-Hindi Example
27
Questions
28
MEMT chart example
Russian leaders signed KBMT (0.8) Russian leaders signed KBMT (0.8) Russian leaders signed KBMT (0.8) Russian leaders signed KBMT (0.8) compact of peace EBMT (0.65) compact of peace EBMT (0.65) compact of peace EBMT (0.65)
political leaders EBMT (0.9) political leaders EBMT (0.9) compact of EBMT (0.7) compact of EBMT (0.7) civilian GLOSS (1.0)
tactful DICT (1.0) pact GLOSS (1.0) of peace EBMT (1.0) of peace EBMT (1.0) civil GLOSS (1.0)
expedients DICT (1.0) bargain DICT (1.0) for DICT (1.0) civil peace EBMT (0.9) civil peace EBMT (0.9)
political DICT (1.0) Russians DICT (1.0) subscribe DICT (1.0) pact DICT (1.0) of GLOSS (1.0) quiet DICT (1.0) civilian DICT (1.0)
leaders DICT (1.0) politic DICT (1.0) Russian DICT (1.0) sign DICT (1.0) compact DICT (1.0) of DICT (1.0) peace DICT (1.0) civil DICT (1.0)
lideres politicos rusos firman pacto de paz civil
29
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)

30
English-Chinese Example
31
Spanish-Mapudungun Example
32
English-Arabic Example
33
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

34
Transfer Rule Formalism
SL the old man, TL ha-ish ha-zaqen NPNP
DET ADJ N -gt DET N DET ADJ ( (X1Y1) (X1Y3)
(X2Y4) (X3Y2) ((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))

35
Transfer Rule Formalism (II)
SL the old man, TL ha-ish ha-zaqen NPNP
DET ADJ N -gt DET N DET ADJ ( (X1Y1) (X1Y3)
(X2Y4) (X3Y2) ((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

36
The Transfer Engine
Analysis Source text is parsed into its grammatical structure. Determines transfer application ordering. Example ? ? ??(he read book) S NP VP N V NP ? ? ? Transfer A target language tree is created by reordering, insertion, and deletion. S NP VP N V NP 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
37
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

38
Flat Seed Rule Generation
Learning Example NP Eng the big apple Heb ha-tapuax ha-gadol
Generated Seed Rule NPNP ART ADJ N ? ART N ART ADJ ((X1Y1) (X1Y3) (X2Y4) (X3Y2))
39
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 f-structure
y-side constraints TL dictionary, aligned SL words (list of projecting features)
40
Compositionality
Initial Flat Rules SS ART ADJ N V ART N ? ART N ART ADJ V P ART N ((X1Y1) (X1Y3) (X2Y4) (X3Y2) (X4Y5) (X5Y7) (X6Y8)) NPNP ART ADJ N ? ART N ART ADJ ((X1Y1) (X1Y3) (X2Y4) (X3Y2)) NPNP ART N ? ART N ((X1Y1) (X2Y2))
Generated Compositional Rule SS NP V NP ? NP V P NP ((X1Y1) (X2Y2) (X3Y4))
41
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

42
Seeded Version Space Learning
Input Rules and their Example Sets SS NP V NP ? NP V P NP ex1,ex12,ex17,ex26 ((X1Y1) (X2Y2) (X3Y4)) NPNP ART ADJ N ? ART N ART ADJ ex2,ex3,ex13 ((X1Y1) (X1Y3) (X2Y4) (X3Y2)) NPNP ART N ? ART N ex4,ex5,ex6,ex8,ex10,ex11 ((X1Y1) (X2Y2))
Output Rules with Feature Constraints SS NP V NP ? NP V P NP ((X1Y1) (X2Y2) (X3Y4) (X1 NUM X2 NUM) (Y1 NUM Y2 NUM) (X1 NUM Y1 NUM))
43
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

44
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))

45
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

46
Seeded Version Space LearningThe 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

47
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?

48
Examples of Learned Rules (Hindi-to-English)
NP,14244 Score0.0429 NPNP N -gt DET N ( (X1Y2) )
NP,14434 Score0.0040 NPNP ADJ CONJ ADJ N -gt ADJ CONJ ADJ N ( (X1Y1) (X2Y2) (X3Y3) (X4Y4) )
PP,4894Score0.0470PPPP NP POSTP -gt PREP NP((X2Y1)(X1Y2))
49
Manual Transfer Rules Hindi Example
PASSIVE OF SIMPLE PAST (NO AUX) WITH LIGHT
VERB passive of 43 (7b) VP,28 VPVP V V
V -gt Aux V ( (X1Y2) ((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) )
50
Manual Transfer Rules Example
NP PP NP1 NP P Adj N
N1 ke eka aXyAya N
jIvana
NP NP1 PP Adj N
P NP one chapter of N1
N life
NP1 ke NP2 -gt NP2 of NP1 Ex jIvana ke
eka aXyAya life of (one) chapter
gt a chapter of life NP,12 NPNP PP
NP1 -gt NP1 PP ( (X1Y2) (X2Y1) ((x2
lexwx) 'kA') ) NP,13 NPNP NP1 -gt
NP1 ( (X1Y1) ) PP,12 PPPP NP Postp
-gt Prep NP ( (X1Y2) (X2Y1) )
51
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 17589 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!!

52
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)

53
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!

54
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
• XFERSMT with strong decoder (MEMT)

55
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 IBMs 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

56
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
XFERSMT 0.136 0.243 5.65
57
Effect of Reordering in the Decoder

58
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 0.6989
• Token coverage with morphology 0.7892
• 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

59
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

60
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

61
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 HBW in
committee the elections the central et the
possibility conduct poll crowd about TWKNIT the
NSIGH from goat
62
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

63
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

64
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

65

• 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