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Minimum Risk Annealing for Training Log Linear Models

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Figure

Figure 1: The loss surface for a machine translation sys-tem: while other parameters are held constant, we vary theweights on the distortion and word penalty features
Figure 2: Loss and expected loss as one translation model’sweight varies: the gray line (γ=∞) shows true BLEU (to beoptimized in equation (2))
Table 1:BLEU 4n1 percentage on translating 2000-sentence test corpora, after training the 8 experts on 100,000sentence pairs and fitting their weights θ on 200 more, usingsettings tuned on a further 200
Figure 3: Iterative change in BLEU on German-English de-velopment (upper) and held-out (lower), under annealed min-imum risk training with different starting temperatures, ver-sus minimum error training with 10 random restarts.
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