Inferring Shallow-Transfer Machine Translation Rules from Small Parallel Corpora
This addresses the challenge of reducing manual effort in rule-based machine translation for language pairs with limited resources, though it is incremental as it builds on existing alignment template methods.
The paper tackles the problem of automatically inferring structural transfer rules for shallow-transfer machine translation from small parallel corpora, showing that translation quality improves compared to word-for-word translation and approaches that of hand-coded rules in experiments with three language pairs.
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.