Paper: A Path-Based Transfer Model For Machine Translation

ACL ID C04-1090
Title A Path-Based Transfer Model For Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

We propose a path-based transfer model for machine translation. The model is trained with a word-aligned parallel corpus where the source language sentences are parsed. The training algorithm extracts a set of transfer rules and their probabilities from the training corpus. A rule translates a path in the source language dependency tree into a fragment in the target dependency tree. The problem of finding the most probable translation becomes a graph-theoretic problem of finding the minimum path covering of the source language dependency tree.