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- W2149338434 abstract "Contrary to popular belief, we show that the optimal parameters for IBM Model 1 are not unique. We demonstrate that, for a large class of words, IBM Model 1 is indifferent among a continuum of ways to allocate probability mass to their translations. We study the magnitude of the variance in optimal model parameters using a linear programming approach as well as multiple random trials, and demonstrate that it results in variance in test set log-likelihood and alignment error rate." @default.
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- W2149338434 date "2011-06-19" @default.
- W2149338434 modified "2023-09-24" @default.
- W2149338434 title "Why Initialization Matters for IBM Model 1: Multiple Optima and Non-Strict Convexity" @default.
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