Entropy-based pruning has a limit in selecting a fine distribution of phrase pairs to be pruned in a threshold.

Changing the distribution through other divergence metrics improves pruning efficiency in our preliminary empirical analysis.

Derived problematic factors are fixed divergence distribution and missing impact of word-coupling strength.

We propose a fine pruning method using two parameters to control the factors and analyze their effects to divergence change.

It improves pruning efficiency compared with Entropy-based pruning in practical translations of English, Spanish, and French.