Better statistical estimation can benefit all phrases in phrase-based statistical machine translation

The heuristic estimates of conditional phrase translation probabilities are based on frequency counts in a word-aligned parallel corpus. Earlier attempts at more principled estimation using Expectation-Maximization (EM) under perform this heuristic. This paper shows that a recently introduced novel estimator based on smoothing might provide a good alternative. When all phrase pairs are estimated (no length cut-off), this estimator slightly outperforms the heuristic estimator.

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