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 Presentation

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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.

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!

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

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!

Phrase-Based Alignment

The Pharaoh Model (in abstract)

The Pharaoh Model (in detail)

Bidirectional Alignment

Heuristic: Grow Diagonally

Heuristic: Attach Neighbors

Alignment Heuristics

Phrase Size

Alternative:Joint Phrase Alignment Model

Sources of Alignment

Lexical Weighting

How to Translate?

The Pharaoh Decoder

The Pharaoh Decoder

Hypothesis Lattices

Pruning

but not admissible

Significance

Inspired fruitful follow-up work involving phrase-based and syntax-based statistical MT.

Next

Co-reference Resolution