For each system, we used three different alignment heuristics (grow, grow-diag, grow-diag-final4) to obtain the final alignment results, and then constructed three different phrase tables .

In order to further analyze the translation results, we evaluated the above systems by examining the coverage of the phrase tables over the test phrases.

The first is based on phrase table multiplication (Cohn and Lapata 2007; Wu and Wang, 2007).

It multiples corresponding translation probabilities and lexical weights in source-pivot and pivot-target translation models to induce a new source-target phrase table .

When (re-)training the models, two phrase tables are learned for each SMT model: one from the labeled data 11.. and the other one from pseudo-labeled data lU+ (which we call the main and auxiliary phrase tables respectively).

Some of these fragments are the source language part of a phrase pair available in the phrase table , which we call regular phrases and denote their set by X £69 for a sentence 3.

However, there are some fragments in the sentence which are not covered by the phrase table —possibly because of the OOVs (out-of-vocabulary words) or the constraints imposed by the phrase extraction algorithm — called X 800” for a sentence 5.