We combine the HMM alignment, the BM alignment and the MaXEnt alignment (ME) using the above link selection algorithm.

Figure 3 shows such an example, where alignment errors in the MaXEnt alignment are shown with dotted lines.

In addition to the alignment combination, we also improve the performance of the MaXEnt aligner through confidence-based alignment link filtering.

Here we select the MaXEnt aligner because it has

In section 4 we show how to improve a MaXEnt word alignment quality by removing low confidence alignment links, which also leads to improved translation quality as shown in section 5.

Regarding word alignment combination, in addition to the commonly used ”intersection-union-refine” approach (Och and Ney, 2003), (Ayan and Dorr, 2006b) and (Ayan et al., 2005) combined alignment links from multiple word alignment based on a set of linguistic and alignment features within the MaXEnt framework or a neural net model.

HMM 54.72 -0.710 BM 62.53 -0.699 MaxEnt 69.26 -0.699

We randomly selected 512 Chinese-English (CE) sentence pairs and generated word alignment using the MaxEnt aligner (Ittycheriah and Roukos, 2005).

For each sentence pair in the CE test set, we calculate the confidence scores of the HMM alignment, the Block Model alignment and the MaXEnt alignment, then select the alignment with the highest confidence score.

We extract phrase translation tables from the baseline MaXEnt word alignment as well as the alignment with confidence-based link filtering, then translate the test set with each phrase translation table.

The standard Viterbi decoding objective is to find 6* = arg maxe A - 6( f, e).

6 = arg maxe EP(e/|f) [8(6; (3’)] arg maxe Z P(e’|f) - 8(6; 6’)

arg maerE EP(e’|f) [3(6; 6/” = arg maxe Z P(e/|f) - ij'(€) ° ¢j(el) e’EE j