Automatic Alignment of Czech and English Deep Syntactic Dependency Trees

Abstract

In this paper, we focus on alignment of Czech and English tectogrammatical dependency trees. The alignment of deep syntactic de- pendency trees can be used for training transfer models for machine translation systems based on analysis-transfer-synthesis architecture.

The results of our experiments show that shifting the alignment task from the word layer to the tectogrammatical layer both (a) increases the inter- annotator agreement on the task and (b) allows to construct a feature- based algorithm which uses sentence structure and which outperforms the GIZA++ aligner in terms of f-measure on aligned tectogrammatical node pairs.