cs 479, section 1: natural language processing
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This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License . CS 479, section 1: Natural Language Processing. Lecture # 38: Phrase-based Translation. Lecture content by Eric Ringger, Dan Klein of UC Berkeley, and Phillip Koehn formerly of ISI. - PowerPoint PPT PresentationTRANSCRIPT
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Lecture content by Eric Ringger, Dan Klein of UC Berkeley, and Phillip Koehn formerly of ISI.
CS 479, section 1:Natural Language Processing
Lecture #38: Phrase-based Translation
This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.
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Announcements Reading Report #14 on phrase-based translation
Due: Wednesday (online) Last one!
Final Project Reports Due: today
Last day to submit work The last day of instruction for the semester (Thursday), 12/6
Final Exam: Comprehensive Review in Class on Wednesday Come prepared with your questions!
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Objectives
Understand phrase-based methods for statistical MT
See (near) state-of-the-art results for MT from the phrase-based approach
See a negative result for syntax in statistical MT
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Phrases in Word-Alignment Models
Target:
Source:
Restriction: multiple words in the source language can align with a single word in the target language, but not the other way around.
For word-alignment models, Direction Matters!
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Phrase-Based Alignment
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The Pharaoh Model (in abstract)
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The Pharaoh Model (in detail)
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Bidirectional Alignment
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Heuristic: Grow Diagonally
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Heuristic: Attach Neighbors
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Alignment Heuristics
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Phrase Size
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Alternative:Joint Phrase Alignment Model
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Sources of Alignment
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Lexical Weighting
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How to Translate?
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The Pharaoh Decoder
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The Pharaoh Decoder
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Hypothesis Lattices
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Pruning
but not admissible
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Significance
Inspired fruitful follow-up work involving phrase-based and syntax-based statistical MT.
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Next
Co-reference Resolution