IBM alignment models: Difference between revisions

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Model 3: example
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\sum_x t'(e_x | f_y) = 1 \quad \forall y
\end{cases}
</math>This can be solved by [[Lagrange multiplier|Lagrangian multipliers]], then simplified. For a detailed derivation of the algorithm, see <ref name=":0">{{Cite book |last=Koehn |first=Philipp |url=https://books.google.com/books?id=4v_Cx1wIMLkC&newbks=0&hl=en |title=Statistical Machine Translation |date=2010 |publisher=Cambridge University Press |isbn=978-0-521-87415-1 |language=en |chapter=4. Word-Based Models}}</ref> chapter 4 and .<ref>{{Cite web |title=CS288, Spring 2020, Lectur 05: Statistical Machine Translation |url=https://cal-cs288.github.io/sp20/slides/cs288_sp20_05_statistical_translation_1up.pdf |url-status=live |archive-url=https://web.archive.org/web/20201024011801/https://cal-cs288.github.io/sp20/slides/cs288_sp20_05_statistical_translation_1up.pdf |archive-date=24 Oct 2020}}</ref>.
 
In short, the EM algorithm goes as follows:<blockquote>INPUT. a corpus of English-foreign sentence pairs <math>\{(e^{(k)}, f^{(k)})\}_k</math>