from bits to bots: women everywhere, leading the way
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FROM BITS TO BOTS: Women Everywhere, Leading the Way. Lenore Blum, Anastassia Ailamaki, Manuela Veloso, Sonya Allin, Bernardine Dias, Ariadna Font Llitjós School of Computer Science Carnegie Mellon University. AVENUE Automatic Machine Translation for low-density languages. - PowerPoint PPT PresentationTRANSCRIPT
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FROM BITS TO BOTS: Women Everywhere, Leading
the Way
Lenore Blum, Anastassia Ailamaki, Manuela Veloso, Sonya Allin,
Bernardine Dias, Ariadna Font Llitjós
School of Computer Science
Carnegie Mellon University
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AVENUEAutomatic Machine
Translation for low-density languages
Ariadna Font Llitjós
Language Technologies Institute
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Automatic Machine Translation
Interlingua
Transfer rules
Corpus-based methodsanalysis
interpretation
generation
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Low-density languages
• Not endangered languages, but languages with little or no presence in the web, little or no linguistic resources
• AVENUE is currently working with:– Mapudungun [Chile]– Inupiaq [Alaska]– Aymara, Quechua and Aguaruna [Peru]– Siona [Colombia]
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Mapudungun for the Mapuche
ChileOfficial Language: SpanishPopulation: ~15 million
~1/2 million Mapuche people
Language: Mapudungun
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The language: Mapudungun
• Oral tradition (170 hours of recorded speech in the medical domain)
• Just a few written texts exist• Need to standardize the alphabet, determine
phoneme set and writing rules, develop an electronic dictionary
• We provide them with linguistic and technical advice + tools such as a morphological analyzer, parser and ultimately an MTS
• We work in collaboration with a local team in Temuco
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Our last meeting in Temuco, May 2002
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New approach to MT
• Fully automatic (no human intervention)
• Very little electronic data available elicitation corpus
• Machine learning techniques– Seeded version space algorithm to
automatically learn transfer rules– Interactive and Automatic refinement of
Transfer rules
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Elicitation corpus sample…\spa Una mujer se quedó en casa\map Kie domo mlewey ruka mew\eng One woman stayed at home.
\spa V una mujer\map Pen kie domo\eng I saw one woman.
\spa Hay suficiente comida para una mujer\map Mley iagel i yochiluwam kie domo\eng There is enough food for one woman.…
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Automatic Learning of a Transfer-based MTS
Elicitation corpus
SVS algorithm
Transfer module
tentativeTransfer
rules
Rule Refinement
module
SL sentences(tentative)
TL sentences
Kathrin Probst
Erik Peterson Ariadna Font
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Interactive and Automatic rule refinement
1. Given an MTS, translate sentences and present them to the users for minimal correction (interface design, MT error classification)
2. Determine blame assignment
3. Structure learning, as opposed to binary feedback, to automatically refine the existing rules
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Interactive Learning• Translation Correction Tool, web application• Bilingual informants (no knowledge of
linguistics assumed)• User-friendly and Intuitive interface
• Can naïve users reliably pinpoint the source of errors? MT error classification realistic?
• Need of user studies:– Spanish - English– English - Spanish– English - Chinese
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Structure learning
• Given user feedback (correction + error classification) and blame assignment, modify the appropriate transfer rule(s) to obtain correct translation
• Need to evaluate based on cross-validation, number of sentences it can translate correctly (elicitation corpus)
Learn mapping between incorrect structures and correct structures:
She saw high woman She saw the tall woman
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A simple example Spanish SLS: Ella vio a la mujer altaEnglish TLS: She saw high womanCorrected TLS: She saw the tall woman
• MT error classification: missing determiner + wrong lexical selection
• Blame assignment (NP rule that generated the direct object + selectional restrictions)
• Rule refinement: the Noun Phrase (NP) rule that generated the error:
NP -> Adj Nneeds to be refined into 2 different cases:
NP -> Det Adj N[sg] (the tall woman)NP -> (Det) Adj N[pl] ((the)? tall women)
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AVENUE project members LTI team:
Researchers Ph. D. students Jaime Carbonell Ariadna Font Llitjós Lori Levin Christian Monson Alon Lavie Erik Peterson
Ralf Brown Katharina Probst Avenue External Project Coordinator Rodolfo M Vega,
Chilean team:Eliseo Cañulef Luis Caniupil Huaiquiñir Hugo Carrasco Marcela Collio Calfunao Rosendo Huisca Cristian Carrillan Anton
Hector Painequeo Salvador Cañulef Flor Caniupil Claudio Millacura
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Thanks!
For more information:
http://www.cs.cmu.edu/~aria/avenue/