lect17-mt.key - Machine Translation Lecture #17...

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Machine Translation Lecture #17 Introduction to Natural Language Processing CMPSCI 585, Fall 2007 Andrew McCallum (including slides from Michael Collins, and Dan Klein)
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The challenges of Machine Translation
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Lexical Ambiguity Example 1: book the flight reservar read the book libro Example 2: the box was in the pen the pen was on the table Example 3: kill a man matar kill a process acabar
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Differing Word Orders English word order is subject – verb – object Japanese word order is subject – object – verb English: IBM bought Lotus Japanese: IBM Lotus bought English: Sources said that IBM bought Lotus yesterday Japanese: Sources yesterday IBM Lotus bought that said
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Syntactic Structure is not Preserved Across Translations The bottle floated into the cave La botella entro a la cuerva flotando (the bottle entered the cave floating)
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Syntactic Ambiguity Causes Problems John hit the dog with the stick John golpeo el perro con el palo/que tenia el palo
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Pronoun Resolution The computer outputs the data; it is fast. La computadora imprime los datos; es rapida The computer outputs the data; it is stored in ascii. La computadora imprime los datos; estan almacendos en ascii
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Differing Treatments of Tense From Dorr et. al 1998: Mary went to Mexico. During her stay she learned Spanish. Went iba (simple past/preterit) Mary went to Mexico. When she returned she started to speak Spanish. fue (ongoing past/imperfect)
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The Best Translation May not be 1-1 (From Manning and Schuetze) : According to our survey, 1988 sales of mineral water and soft drinks were much higher than in 1987, reflecting the growing popularity of these products. Cola drink manufacturers in particular achieved above average growth rates. Quant aux eauxminerales et aux limonades, ellesrecontrent toujours plus d’adeptes. En effet notre sondage fait ressortir des ventes nettement superieures a celles de 1987, pour les boissons a base de cola notamment. With regard to the mineral waters and the lemonades (soft drinks) they encounter still more users. Indeed our survey makes stand out the sales clearly superior to those in 1987 for cola-based drinks especially.
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Machine Translation: Example
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History 1950’s: Intensive research activity in MT 1960’s: Direct word-for-word replacement 1966 (ALPAC): NRC Report on MT Conclusion: MT no longer worthy of serious scientific investigation. 1966-1975: `Recovery period’ 1975-1985: Resurgence (Europe, Japan) 1985-present: Gradual Resurgence (US) http://ourworld.compuserve.com/homepages/WJHutchins/MTS-93.htm
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Levels of Transfer Interlingua Semantic Structure Semantic Structure Syntactic Structure Syntactic Structure Word Structure Structure Source Text Target Text Semantic Composition Semantic Decomposition Semantic Analysis Semantic Generation Syntactic Analysis Syntactic Generation Morphological Analysis Morphological Generation Semantic Transfer Syntactic Transfer Direct (Vauquois triangle)
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General Approaches Rule-based approaches Expert system-like rewrite systems Interlingua methods (analyze and generate)
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lect17-mt.key - Machine Translation Lecture #17...

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