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Unformatted text preview: The old man is happy. He has fished many 1mes. 2.  His wife talks to him. 3.  The sharks await. El viejo está feliz porque ha pescado muchos veces. Su mujer habla con él. Los 1burones esperan. Dan Jurafsky Steps 3 and 4: Crea$ng Phrase Alignments from Word Alignments •  Word alignments are one ­to ­many •  We need phrase alignment (many ­to ­many) •  To get phrase alignments: 1)  We first get word alignments for both E→F and F →E 2)  Then we “symmetrize” the two word alignments into one set of phrase alignments Dan Jurafsky Regular 1 ­to ­many alignment 49 Dan Jurafsky Alignment from IBM Model 1 run on reverse pairing 50 Dan Jurafsky •  Compute intersec1on •  Then use heuris1cs to add points from the union •  Philipp Koehn 2003. Noun Phrase Transla1on 51 Dan Jurafsky Step 5. Extrac$ng phrases from the resul$ng phrase aligment Extract all phrases that are consistent with the word alignment (Maria, Mary), (no, did not), (slap, dió una bofetada), (verde, green), (a la, the) (Maria no, Mary did not), (no dió una bofetada, did not slap), (dió una bofetada a la, slap the), (bruja verde, green witch), (a la bruja verde, the green witch) … 52 Dan Jurafsky Final step: The Transla$on Phrase Table •  Goal: A phrase table: •  A set of phrases: f , e •  With a weight for each: ! ( f , e ) •  Algorithm •  Given the phrase aligned bitext •  And all extracted phrases •  MLE es1mate of φ: just count and divide count( f , e ) φ( f , e) = ∑ count( f , e ) f Machine Translation Phrase Alignments and the Phrase Table Machine Translation Phrase-Based Translation Dan Jurafsky Phrase ­Based Transla$on (Koehn et al. 2003) •  Remember the noisy channel model is backwards: We translate German to English by pretending an Engli...
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This document was uploaded on 02/14/2014.

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