The document discusses simplification and explicitation universals in translation studies. It outlines studies that both support and contradict the tendency for translations to simplify language. Regarding explicitation, some studies found translations introduce more explicit information than originals to avoid misinterpretation, though the direction of translation also influences these phenomena. In conclusion, while simplification is not fully confirmed, explicitation occurs often but implicitation must also be considered. These universals can improve applications like machine translation and plagiarism detection.
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This presentation is about "Author"and "Translator". If the Author becomes the translator of his/her own Work, then how far it is considered a good translation or not? Let's find out. Please comment after viewing the presentation..Thanks in advance.
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Computer Assisted Language Learning (CALL) has seen a rise in recent years of software applications that use Automatic Speech Recognition (ASR) to provide pronunciation feedback for language learners. ASR-CALL systems process large amounts of natural language data to provide immediate and individual feedback on the pronunciation accuracy of recorded learner utterances. In order for ASR-CALL systems to be implemented successfully and become effective tools for pronunciation training, understanding learner beliefs and perceptions about pronunciation and technology is key. While recent studies have investigated levels of learner satisfaction and engagement with ASR-CALL systems, little research has been done to date which investigates specific learner characteristics and beliefs which affect learner usage of ASR-CALL systems.
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Whether the user is interacting with a mobile device, a web site, or a phone-based health technology system, there is often a large gap between what the user wants to accomplish and how they want to accomplish it, and what they actually get from the system. The interface can be challenging and capturing any feedback or user interactions is difficult using on keyboards and point and click tools. Speech Recognition is changing this interaction by capturing the clinical input and allowing clinicians and healthcare users to access systems that listen and responds seamlessly understanding the context and the intent turning what the users wants into what they get.
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Simplification and Explicitation Universals
1. Translation Studies
Simplification and Explicitation Universals
Claudiu Mih˘il˘
a a
Faculty of Computer Science
”Alexandru Ioan Cuza” University of Ia¸i
s
21 April 2010
3. Motivation
• The questions
◦ Is there a difference between original and translated language?
◦ If so, is it automatically detectable?
◦ And if so, does it improve NLP quality?
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4. Motivation
• The questions
◦ Is there a difference between original and translated language?
◦ If so, is it automatically detectable?
◦ And if so, does it improve NLP quality?
• The answers
◦ Yes!
◦ Yes: up to 97.62% for simplification
◦ Yes:
• Human translator (self-)assessment
• Statistical machine translation
• Multilingual plagiarism detection
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6. Translation studies
• Specific lexico-grammatical and syntactic characteristics
• Translationese - Gellerstam (1986)
◦ ”Fingerprints” left behind by the translation process
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7. Translation studies
• Specific lexico-grammatical and syntactic characteristics
• Translationese - Gellerstam (1986)
◦ ”Fingerprints” left behind by the translation process
• Translation laws - Toury (1983)
◦ Standardisation, Interference
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8. Translation studies
• Specific lexico-grammatical and syntactic characteristics
• Translationese - Gellerstam (1986)
◦ ”Fingerprints” left behind by the translation process
• Translation laws - Toury (1983)
◦ Standardisation, Interference
• Translation universals - Baker (1993)
◦ Simplification, Explicitation, Convergence, Normalisation
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10. Simplification
• Tendency to produce simpler and easier-to-follow texts
• Laviosa (2002)
◦ Study on small corpus
◦ Features for simplification
◦ Insufficient evidence
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11. Simplification pros
• Baroni (2006)
◦ Detect originals and translations in an Italian corpus
◦ Uni-, bi-, tri-grams, word forms, lemmas, and POS tags
◦ Supervised learning system
◦ Accuracy up to 87%
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12. Simplification pros
• Baroni (2006)
◦ Detect originals and translations in an Italian corpus
◦ Uni-, bi-, tri-grams, word forms, lemmas, and POS tags
◦ Supervised learning system
◦ Accuracy up to 87%
• Corpas (2008a)
◦ English-into-Spanish and Spanish medical and technical texts
◦ Validated for lexical richness
◦ Contradicted for complex sentences, sentence length, ambiguity,
information load, depth of syntactic trees
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13. Simplification pros
• Baroni (2006)
◦ Detect originals and translations in an Italian corpus
◦ Uni-, bi-, tri-grams, word forms, lemmas, and POS tags
◦ Supervised learning system
◦ Accuracy up to 87%
• Corpas (2008a)
◦ English-into-Spanish and Spanish medical and technical texts
◦ Validated for lexical richness
◦ Contradicted for complex sentences, sentence length, ambiguity,
information load, depth of syntactic trees
• Corpas (2008b)
◦ Validated for lexical richness and density, number of discourse
markers, complex sentences, sentence length
◦ More visible for technical domain
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14. Simplification pros
• Ilisei (2010)
◦ 21 language-independent features
◦ Supervised machine learning - 8 classifiers
◦ Accuracy of 97.62%
◦ Most salient features - InfoGain, ChiSquare
• Lexical richness
• Sentence length
• Proportions of pronouns, conjunctions, grammatical and lexical words
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15. Simplification cons
• Jantunen (2001)
◦ Boosters in Finnish translations - hyvin, kovin, oikein
◦ typical lexical combinations in most cases
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16. Simplification cons
• Jantunen (2001)
◦ Boosters in Finnish translations - hyvin, kovin, oikein
◦ typical lexical combinations in most cases
• Jantunen (2004)
◦ Boosters in Finnish translations - hyvin, kovin, oikein
◦ untypical lexical combinations in translations
◦ similar colligations in originals and translations
8 of 13
18. Explicitation
• Introducing overt information into the translation that is implicit in
the source language
• Classification - Pym (2005)
◦ Obligatory explicitation
• Forced by language specificity or grammar
◦ Voluntary explicitation
• Optional information to avoid misinterpretations
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20. Explicitation pros
• Burnett (1999)
◦ BNC vs. TEC
◦ suggest, admit, claim, think, believe, hope, know
• Olohan (2000)
◦ BNC vs. TEC
◦ say / tell + that / zero connective
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21. Explicitation pros
• Burnett (1999)
◦ BNC vs. TEC
◦ suggest, admit, claim, think, believe, hope, know
• Olohan (2000)
◦ BNC vs. TEC
◦ say / tell + that / zero connective
• Olohan (2001)
◦ BNC vs. TEC
◦ promise + that / zero connective
10 of 13
22. Explicitation cons
• Cheong (2006)
◦ Explicitation vs. implicitation
◦ English-into-Korean translations
◦ The phenomena appear equally
◦ The direction of translation influences their behaviour
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23. Conclusions
• Simplification
◦ Many studies supporting it
◦ Many studies contradicting it
◦ Not yet clearly confirmed
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24. Conclusions
• Simplification
◦ Many studies supporting it
◦ Many studies contradicting it
◦ Not yet clearly confirmed
• Explicitation
◦ Occuring often to avoid misinterpretations
◦ Implicitation needs to be considered as well
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25. Conclusions
• Simplification
◦ Many studies supporting it
◦ Many studies contradicting it
◦ Not yet clearly confirmed
• Explicitation
◦ Occuring often to avoid misinterpretations
◦ Implicitation needs to be considered as well
• Usefulness
◦ SMT
◦ Multilingual plagiarism detection
◦ (Self-)assessment of translators’s work
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