  This paper describes a method for the automatic inference of structural
transfer rules to be used in a shallow-transfer machine translation (MT) system
from small parallel corpora. The structural transfer rules are based on
alignment templates, like those used in statistical MT. Alignment templates are
extracted from sentence-aligned parallel corpora and extended with a set of
restrictions which are derived from the bilingual dictionary of the MT system
and control their application as transfer rules. The experiments conducted
using three different language pairs in the free/open-source MT platform
Apertium show that translation quality is improved as compared to word-for-word
translation (when no transfer rules are used), and that the resulting
translation quality is close to that obtained using hand-coded transfer rules.
The method we present is entirely unsupervised and benefits from information in
the rest of modules of the MT system in which the inferred rules are applied.
