Gema Ramírez-Sánchez

Apertium: a free/open-source platform for rule-based machine translation

Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.

Open-Source portuguese–spanish machine translation

This paper describes the current status of development of an open-source shallow-transfer machine translation (MT) system for the [European] Portuguese

Abu-MaTran at WMT 2014 Translation Task: Two-step Data Selection and RBMT-Style Synthetic Rules

\leftrightarrow

An Open-Source Shallow-Transfer Machine Translation Engine for the Romance Languages of Spain

Spanish language pair, developed using the OpenTrad Apertium MT toolbox (www.apertium.org). Apertium uses finite-state transducers for lexical processing, hidden Markov models for part-of-speech tagging, and finite-state-based chunking for structural transfer, and is based on a simple rationale: to produce fast, reasonably intelligible and easily correctable translations between related languages, it suffices to use a MT strategy which uses shallow parsing techniques to refine word-for-word MT. This paper briefly describes the MT engine, the formats it uses for linguistic data, and the compilers that convert these data into an efficient format used by the engine, and then goes on to describe in more detail the pilot Portuguese

Construcción y minimización eficiente de transductores de letras a partir de diccionarios con paradigmas

\leftrightarrow

Development of a free Basque to Spanish machine translation system

Spanish linguistic data.

Re-assessing the Impact of SMT Techniques with Human Evaluation: a Case Study on English - Croatian.

This paper presents the machine translation systems submitted by the AbuMaTran project to the WMT 2014 translation task. The language pair concerned is English‐French with a focus on French as the target language. The French to English translation direction is also considered, based on the word alignment computed in the other direction. Large language and translation models are built using all the datasets provided by the shared task organisers, as well as the monolingual data from LDC. To build the translation models, we apply a two-step data selection method based on bilingual crossentropy difference and vocabulary saturation, considering each parallel corpus individually. Synthetic translation rules are extracted from the development sets and used to train another translation model. We then interpolate the translation models, minimising the perplexity on the development sets, to obtain our final SMT system. Our submission for the English to French translation task was ranked second amongst nine teams and a total of twenty submissions.