Year: 2007
Type: book-chapter
Abstract: This paper focuses on the inference of structural transfer rules for shallow-transfer machine translation (MT). Transfer rules are generated from alignment templates, like those used in statistical MT, that have been extracted from parallel corpora and extended with a set of restrictions that control their application. The experiments conducted using the open-source MT platform Apertium show that there is a clear improvement in translation quality as compared to word-for-word translation (when no transfer rules are used), and that the resulting translation quality is very close to the one 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. 1
Authors: Felipe Sánchez-Martínez, Mikel L. Forcada
Institution: University of Alicante
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W585912846