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. 1.

This paper describes Apertium: a free/open-source machine translation platform (engine, toolbox and data), its history, its philosophy of design, its technology, the community of developers, the research and business based on it, and its prospects and challenges, now that it is five years old. 1

The availability of machine-readable bilingual linguistic resources is crucial not only for machine translation but also for other applications such as cross-lingual information retrieval. However, the building of such resources demands extensive manual work. This paper describes a methodology to build automatically bilingual dictionaries and transfer rules by extracting knowledge from word-aligned parallel corpora processed with free shallow monolingual resources (morphological analysers and part-of-speech taggers). Experiments for Brazilian Portuguese– Spanish and Brazilian Portuguese– English parallel texts have shown promising results. 1

Abstract. In this paper we present experiments concerned with automatically learning bilingual resources for machine translation: bilingual dictionaries and transfer rules. The experiments were carried out with Brazilian Portuguese (pt), English (en) and Spanish (es) textsintwo parallel corpora: pt–en and pt–es. They were designed to investigate the relevance of two factors in the induction process, namely: (1) the coverage of linguistic resources used when preprocessing the training corpora and (2) the maximum length threshold (for transfer rules) used in the induction process. From these experiments, it is possible to conclude that both factors have an influence in the automatic learning of bilingual resources.