Experiments revealed that the proposed model worked robustly, and outperformed five out of six state-of-the-art abbreviation recognizers.

Experimental results indicate that the proposed models significantly outperform previous abbreviation generation studies.

In addition, we apply the proposed models to the task of abbreviation recognition, in which a model extracts the abbreviation definitions in a given text.

Note that all of the six systems were specifically designed and optimized for this recognition task, whereas the proposed model is directly transported from the generation task.

Compared with the contiguous tree sequence-based model, the proposed model can well handle noncontiguous phrases with any large gaps by means of noncontiguous tree sequence alignment.

Experimental results on the NIST MT-05 Chi-nese-English translation task show that the proposed model statistically significantly outperforms the baseline systems.

With the help of the noncontiguous tree sequence, the proposed model can well capture the noncontiguous phrases in avoidance of the constraints of large applicability of context and enhance the noncontiguous constituent modeling.

As for the above example, the proposed model enables the noncontiguous tree sequence pair indexed as TSPS in Fig.

The proposed model leverages on the strengths of both tree sequence-based and forest-based translation models.

This clearly demonstrates the effectiveness of our proposed model for syntax-based SMT.

This again demonstrates the effectiveness of our proposed model .

In this section, we first explain what a packed forest is and then define the concept of the tree sequence in the context of forest followed by the discussion on our proposed model .