The phrase table extracting procedure is trainable and can be optimized jointly with the translation engine.

As the figure 1 shows, when we increase the threshold by allowing more candidate phrase pair hypothesized as valid translation, we observe the phrase table size increases monotonically.

Figure 1: Thresholding effects on translation performance and phrase table size

Our baseline phrase table training method is the ViterbiExtract algorithm.

We notice that Model-4 based phrase table performs roughly 1% better in terms of both BLEU and METEOR scores than that based on HMM.

Since the translation engine implements a log-linear model, the discriminative training of feature weights in the decoder should be embedded in the whole end-to-end system jointly with the discriminative phrase table training process.

As clear in Table 7, it is important to rerun MERT (on MT02 only) with the augmented phrase table in order to get performance gains.

the MERT weights with different phrase tables .

the baseline phrase table .

Since the obtained translation entries for abbreviations have the same format as the regular translation entries in the baseline phrase table, it is relatively easy to add them into the baseline phrase table .

Specifically, if a translation entry (signatured by its Chinese and English strings) to be added is not in the baseline phrase table , we simply add the entry into the baseline table.