Machine Translation Overview

1
Machine Translation Overview

• Alon Lavie
• Language Technologies Institute
• Carnegie Mellon University
• August 25, 2004

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
MEMT chart example
17
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?

18
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

19
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

20
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

21
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

22
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)

23
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

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Transfer with Strong Decoding
25
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

26
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

27
English-Hindi Example
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Questions