Overview of the Language Technologies Institute and AVENUE Project

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Overview of the Language Technologies Institute
and AVENUE Project

• Jaime Carbonell, Director
• March 2, 2002

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Language Technologies Institute

• Director Jaime Carbonell
• Founded in 1986 as the Center for Machine
  Translation.

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School of Computer Science Carnegie Mellon
University

• Center for Automated Learning and Discovery
• Computer Science Department
• Entertainment Technology Center
• Human-Computer Interaction Institute
• Institute for Software Research, International
• Language Technologies Institute
• Robotics Institute

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LTI Research Areas

• Machine Translation
• Information Retrieval, Summarization, Extraction,
  Topic Detection and Tracking
• Speech Recognition and Synthesis
• Computer Assisted Language Instruction
• Multi-modal Interaction

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Members of LTI

• 18 core faculty
• over 40 students (working on masters and Ph.D.)
• approximately 10 courtesy and adjunct faculty
  (e.g., in machine learning)
• probably around 20 funded projects

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Funding of LTI Projects

• Companies e.g., Caterpillar
• National Science Foundation
• DARPA Defense Advance Research Projects Agency
• Other ATR research institute, Japan

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LTI Degree Programs

• Masters in Language Technologies (MLT) two
  years.
• Ph.D. in Language and Information Technologies
  usually three years after masters degree.
• Graduate Program Director Robert Frederking
  (ref_at_cs.cmu.edu)

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Potential Uses of Language Technologies in
Bilingual Education

• For language instruction
• For teaching subjects other than language

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LT for Language Instruction

• Speech Recognition
• Pronunciation tutor (Eskenazi)
• Reading tutor (Mostow)
• Grammar checking
• Dialogue immersion
• Adventure game
• Blocks world
• Using authentic materials

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LT for bilingual education in indigenous
languages

• Partially automated translation of teaching
  materials (science, history, etc.) into
  indigenous languages.

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The AVENUE Project
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Machine Translation of Indigenous Languages

• Policy makers have access to information about
  indigenous people.
• Epidemics, crop failures, etc.
• Indigenous people can participate in
• Health care
• Education
• Government
• Internet
• without giving up their languages.

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History of AVENUE

• Arose from a series of joint workshops of NSF and
  OAS.
• Workshop recommendations
• Create multinational projects using information
  technology to
• provide immediate benefits to governments and
  citizens
• develop critical infrastructure for communication
  and collaborative research
• training researchers and engineers
• advancing science and technology

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Resources for MT

• People who speak the language.
• Linguists who speak the language.
• Computational linguists who speak the language.
• Text on paper.
• Text on line.
• Comparable text on paper or on line.
• Parallel text on paper or on line.
• Annotated text (part of speech, morphology, etc.)
• Dictionaries (mono-lingual or bilingual) on paper
  or on line.
• Recordings of spoken language.
• Recordings of spoken language that are
  transcribed.
• Etc.

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MT for Indigenous Languages

• Minimal amount of parallel text
• Possibly competing standards for
  orthography/spelling
• Maybe not so many trained linguists
• Access to native informants possible
• Need to minimize development time and cost

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Two Technical Approaches

• Generalized EBMT
• Parallel text 50K-2MB (uncontrolled corpus)
• Rapid implementation
• Proven for major Ls with reduced data

• Transfer-rule learning
• Elicitation (controlled) corpus to extract
  grammatical properties
• Seeded version-space learning

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Architecture Diagram
SL Input
Run-Time Module
Learning Module
SL Parser
EBMT Engine
Elicitation Process
SVS Learning Process
Transfer Rules
Transfer Engine
TL Generator
User
Unifier Module
TL Output
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EBMT Example
English I would like to meet
her. Mapudungun Ayükefun trawüael fey
engu.
English The tallest man is my
father. Mapudungun Chi doy fütra chi wentru
fey ta inche ñi chaw.
English I would like to meet the
tallest man Mapudungun (new)
Ayükefun trawüael Chi doy fütra chi
wentru Mapudungun (correct) Ayüken ñi
trawüael chi doy fütra wentruengu.
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Version Space Learning

• Symbolic learning from and examples
• Invented by Mitchell, refined by Hirsch
• Builds generalization lattice implicitly
• Bounded by G and S sets
• Worse-case exponential complexity (in size of G
  and S)
• Slow convergence rate

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Example of Transfer Rule Lattice
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Seeded Version Spaces

• Generate concept seed from first example
• Generalization-level hypothesis (POS feature
  agreement for T-rules in NICE)
• Generalization/specialization level bounds
• Up to k-levels generalization, and up to j-levels
  specialization.
• Implicit lattice explored seed-outwards

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Complexity of SVS

• O(gk) upward search, where g of
  generalization operators
• O(sj) downward search, where s of
  specialization operators
• Since m and k are constants, the SVS runs in
  polynomial time of order max(j,k)
• Convergence rates bounded by F(j,k)

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Next Steps in SVS

• Implementation of transfer-rule intepreter
  (partially complete)
• Implementation of SVS to learn transfer rules
  (underway)
• Elicitation corpus extension for evaluation
  (under way)
• Evaluation first on Mapudungun MT (next)

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NICE Partners
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Agreement Between LTI and Institute of Indigenous
Studies (IEI), Universidad De La Frontera, Chile

• Contributions of IEI
• Native language knowledge and linguistic
  expertise in Mapudungun
• Experience in bicultural, bilingual education
• Data collection recording, transcribing,
  translating
• Orthographic normalization of Mapudungun

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Agreement between LTI and Institute of Indigenous
Studies (IEI), Universidad de la Frontera, Chile

• Contributions of LTI
• Develop MT technology for indigenous languages
• Training for data collection and transcription
• Partial support for data collection effort
  pending funding from Chilean Ministry of
  Education
• International coordination, technical and project
  management

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LTI/IEI Agreement

• Continue collaboration on data collection and
  machine translation technology.
• Pursue focused areas of mutual interest, such as
  bilingual education.
• Seek additional funding sources in Chile and the
  US.

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The IEI Team

• Coordinator (leader of a bilingual and
  multicultural education project)
• Eliseo Canulef
• Distinguished native speaker
• Rosendo Huisca
• Linguists (one native speaker, one near-native)
• Juan Hector Painequeo
• Hugo Carrasco
• Typists/Transcribers
• Recording assistants
• Translators
• Native speaker linguistic informants

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MINEDUC/IEIAgreement Highlights

• Based on the LTI/IEI agreement, the Chilean
  Ministry of Education agreed to fund the data
  collection and processing team for the year 2001.
  This agreement will be renewed each year, as
  needed.

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MINEDUC/IEI AgreementObjectives

• To evaluate the NICE/Mapudungun proposal for
  orthography and spelling
• To collect an oral corpus that represent the four
  Mapudungun dialects spoken in Chile. The main
  domain is primary health, traditional and western.

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MINEDUC/IEI AgreementDeliverables

• An oral corpus of 800 hours recorded,
  proportional to the demography of each current
  spoken dialect
• 120 hours transcribed and translated from
  Mapudungun to Spanish
• A refined proposal for writing Mapudungun

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Nice/MapudungunDatabase

• Writing conventions (Grafemario)
• Glossary Mapudungun/Spanish
• Bilingual newspaper, 4 issues
• Ultimas Familias memoirs
• Memorias de Pascual Coña
• Publishable product with new Spanish translation
• 35 hours transcribed speech
• 80 hours recorded speech

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NICE/MapudungunOther Products

• Standardization of orthography Linguists at UFRO
  have evaluated the competing orthographies for
  Mapudungun and written a report detailing their
  recommendations for a standardized orthography
  for NICE.
• Training for spoken language collection In
  January 2001 native speakers of Mapudungun were
  trained in the recording and transcription of
  spoken data.

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Underfunded Activities

• Data collection
• Colombia (unfunded)
• Chile (partially funded)
• Travel
• More contact between CMU and Chile (UFRO) and
  Colombia.
• Training
• Train Mapuche linguists in language technologies
  at CMU.
• Extend training to Colombia
• Refine MT system for Mapudungun and Siona
• Current funding covers research on the MT engine
  and data collection, but not detailed linguistic
  analysis

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Outline

• History of MT--See Wired magazine May 2000 issue.
  Available on the web.
• How well does it work?
• Procedure for designing an LT project.
• Choose an application What do you want to do?
• Identify the properties of your application.
• Methods knowledge-based, statistical/corpus
  based, or hybrid.
• Methods interlingua, transfer, direct
• Typical components of an MT system.
• Typical resources required for and MT system.

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How well does it work?Example SpanAm

• Possibly the best Spanish-English MT system.
• Around 20 years of development.

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How well does it work?Example Systran

• Try it on the Altavista web page.
• Many language pairs are available.
• Some language pairs might have taken up to a
  person-century of development.
• Can translate text on any topic.
• Results may be amusing.

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How well does it work?Example KANT

• Translates equipment manuals for Caterpillar.
• Input is controlled English many ambiguities are
  eliminated. The input is checked carefully for
  compliance with the rules.
• Around 5 output languages.
• The output might be post-edited.
• The result has to be perfect to prevent accidents
  with the equipment.

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How well does it work?Example JANUS

• Translates spoken conversations about booking
  hotel rooms or flights.
• Six languages English, French, German, Italian,
  Japanese, Korean (with partners in the C-STAR
  consortium).
• Input is spontaneous speech spoken into a
  microphone.
• Output is around 60 correct.
• Task Completion is higher than translation
  accuracy users can always get their flights or
  rooms if they are willing to repeat 40 of their
  sentences.

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How well does it work?Speech Recognition

• Jupiter weather information 1-888-573-8255. You
  can say things like what cities do you know
  about in Chile? and What will be the weather
  tomorrow in Santiago?.
• Communicator flight reservations 1-877-CMU-PLAN.
  You can say things like Im travelling to
  Pittsburgh.
• Speechworks demo 1-888-SAY-DEMO. You can say
  things like Sell my shares of Microsoft.
• These are all in English, and are toll-free only
  in the US, but they are speaker-indepent and
  should work with reasonable foreign accents.

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Different kinds of MT

• Different applications for example, translation
  of spoken language or text.
• Different methods for example, translation rules
  that are hand crafted by a linguist or rules that
  are learned automatically by a machine.
• The work of building an MT program will be very
  different depending on the application and the
  methods.

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Procedure for planning an MT project

• Choose an application.
• Identify the properties of your application.
• List your resources.
• Choose one or more methods.
• Make adjustments if your resources are not
  adequate for the properties of your application.

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Choose an application What do you want to do?

• Exchange email or chat in Quechua and Spanish.
• Translate Spanish web pages about science into
  Quechua so that kids can read about science in
  their language.
• Scan the web Is there any information about
  such-and-such new fertilizer and water
  pollution? Then if you find something that looks
  interesting, take it to a human translator.
• Answer government surveys about health and
  agriculture (spoken or written).
• Ask directions (where is the library?)
  (spoken).
• Read government publications in Quechua.

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Identify the properties of your application.

• Do you need reliable, high quality translation?
• How many languages are involved? Two or more?
• Type of input.
• One topic (for example, weather reports) or any
  topic (for example, calling your friend on the
  phone to chat).
• Controlled or free input.
• How much time and money do you have?
• Do you anticipate having to add new topics or new
  languages?

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Do you need high quality?

• Assimilation Translate something into your
  language so that you can
• understand it--may not require high quality.
• evaluate whether it is important or interesting
  and then send it off for a better
  translation--does not require high quality.
• use it for educational purposes--probably
  requires high quality.

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Do you need high quality?

• Dissemination Translate something into someone
  elses language e.g., for publication.
• Usually should be high quality.

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Do you need high quality?

• Two-Way e.g., chat room or spoken conversation
• May not require high reliability on correctness
  if you have a native language paraphrase.
• Original input I would like to reserve a double
  room.
• Paraphrase Could you make a reservation for a
  double room.

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Type of Input

• Formal text newspaper, government reports,
  on-line encyclopedia.
• Difficulty long sentences
• Formal speech spoken news broadcast.
• Difficulty speech recognition wont be perfect.
• Conversational speech
• Difficulty speech recognition wont be perfect
• Difficulty disfluencies
• Difficulty non-grammatical speech
• Informal text email, chat
• Difficulty non-grammatical speech

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Methods Knowledge-Based

• Knowledge-based MT a linguist writes rules for
  translation
• noun adjective -- adjective noun
• Requires a computational linguist who knows the
  source and target languages.
• Usually takes many years to get good coverage.
• Usually high quality.

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Methods statistical/corpus-based

• Statistical and corpus-based methods involve
  computer programs that automatically learn to
  translate.
• The program must be trained by showing it a lot
  of data.
• Requires huge amounts of data.
• The data may need to be annotated by hand.
• Does not require a human computational linguist
  who knows the source and target languages.
• Could be applied to a new language in a few days.
• At the current state-of-the-art, the quality is
  not very good.

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Methods Interlingua

• An interlingua is a machine-readable
  representation of the meaning of a sentence.
• Id like a double room/Quisiera una habitacion
  doble.
• request-actionreservationhotel(room-typedouble)
  
• Good for multi-lingual situations. Very easy to
  add a new language.
• Probably better for limited domains -- meaning is
  very hard to define.

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Multilingual Interlingual Machine Translation

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Methods Transfer

• A transfer rule tells you how a structure in one
  language corresponds to a different structure in
  another language
• an adjective followed by a noun in English
  corresponds to a noun followed by an adjective in
  Spanish.
• Not good when there are more than two languages
  -- you have to write different transfer rules for
  each pair.
• Better than interlingua for unlimited domain.

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Methods Direct

• Direct translation does not involve analyzing the
  structure or meaning of a language.
• For example, look up each word in a bilingual
  dictionary.
• Results can be hilarious the spirit is willing
  but the flesh is weak can become the wine is
  good, but the meat is lousy.
• Can be developed very quickly.
• Can be a good back-up when more complicated
  methods fail to produce output.

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Components of a Knowledge-Based Interlingua MT
System

• Morphological analyzer identify prefixes,
  suffixes, and stem.
• Parser (sentence-to-syntactic structure for
  source language, hand-written or automatically
  learned)
• Meaning interpreter (syntax-to-semantics, source
  language).
• Meaning interpreter (semantics-to-syntax, target
  language).
• Generator (syntactic structure-to-sentence) for
  target language.

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Resources for a knowledge-based interlingua MT
system

• Computational linguists who know the source and
  target languages.
• As large a corpus as possible so that the
  linguists can confirm that they are covering the
  necessary constructions, but the size of the
  corpus is not crucial to system development.
• Lexicons for source and target languages, syntax,
  semantics, and morphology.
• A list of all the concepts that can be expressed
  in the systems domain.

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Components of Example Based MT a direct
statistical method

• A morphological analyzer and part of speech
  tagger would be nice, but not crucial.
• An alignment algorithm that runs over a parallel
  corpus and finds corresponding source and target
  sentences.
• An algorithm that compares an input sentence to
  sentences that have been previously translated,
  or whose translation is known.
• An algorithm that pulls out the corresponding
  translation, possibly slightly modifying a
  previous translation.

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Resources for Example Based MT

• Lexicons would improve quality of translation,
  but are not crucial.
• A large parallel corpus (hundreds of thousands of
  words).

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Omnivorous Multi-Engine MT eats any available
resources
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Approaches we had in mind

• Direct bilingual-dictionary lookup because it is
  easy and is a back-up when other methods fail.
• Generalized Example-Based MT because it is easy
  and fast and can be also be a back-up.
• Instructable Transfer-based MT a new, untested
  idea involving machine learning of rules from a
  human native speaker. Useful when computational
  linguists dont know the language, and people who
  know the language are not computational
  linguists.
• Conventional, hand-written transfer rules in
  case the new method doesnt work.