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# Even if we have the right n words there are n

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Unformatted text preview: sh sentence generated a German sentence •  Genera1ve model gives us our probability P(F|E) •  Given a German sentence, ﬁnd the English sentence that generated it. •  Dan Jurafsky Three Components of Phrase ­based MT •  P(F|E) Transla1on model •  P(E): Language model •  Decoder: ﬁnding the sentence E that maximizes P(F|E)P(E) Dan Jurafsky The Transla\$on Model P(F|E) Genera\$ve Model for Phrase ­Based Transla\$on P(F | E): Given English phrases in E, generate Spanish phrases in F. 1.  Group E into phrases ē1, ē2,…,ēI 2.  Translate each phrase ēi, into fi, based on transla)on probability φ(fi | ēi) 3.  Reorder each Spanish phrase fi based on its distor)on probability d. I P( F | E ) = ! ! ( fi , ei )d (starti " endi"1 " 1) i=1 Dan Jurafsky Distor\$on Probability: Distance ­based reordering •  Reordering distance: how many words were skipped (either forward or backward) when genera1ng the next foreign word. •  •  •  •  •  •  starti: the word index of the ﬁrst word of the foreign phrase that translates the ith English phrase endi: the word index of the last word of the foreign phrase that translates the ith English phrase Reordering distance = starti-endi-1-1 What is the probability that a phrase in the English sentence skips over x Spanish words in the Spanish sentence? Two words in sequence: starti=endi ­1+1, so distance=0. How are d probabili1es computed? •  Exponen1ally decaying cost func1on: d ( x ) = ! |x| •  Where ! ! [ 0, 1] Dan Jurafsky Sample Transla\$on Model Position 1 2 3 4 5 6 English Mary did not slap the green witch Spanish Maria no dió una bofetada a la bruja verde 0 0 1 -2 starti−endi−1-1 0 0 p( F | E ) = ! (Maria,Mary)" 0! (no, did not )" 0! (dio una bofetada a, slap)" 0 ! (la,the)" 0! (verde,green )" 1! (bruja,witch )" 2 Dan Jurafsky The goal of the decoder •  The best En...
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