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.
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. Small compared to the size of corpora commonly used to build corpus-based MT systems (Och, 2005).
Abstract. This paper describes the current status of development of an open-source shallow-transfer machine translation (MT) system for the [European] Portuguese ↔ 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↔Spanish linguistic data.
This paper describes the Universitat d'Alacant submissions (labelled as UAlacant) for the machine translation quality estimation (MTQE) shared task in WMT 2015, where we participated in the wordlevel MTQE sub-task. The method we used to produce our submissions uses external sources of bilingual information as a black box to spot sub-segment correspondences between a source segment S and the translation hypothesis T produced by a machine translation system. This is done by segmenting both S and T into overlapping subsegments of variable length and translating them in both translation directions, using the available sources of bilingual information on the fly. For our submissions, two sources of bilingual information were used: machine translation (Apertium and Google Translate) and the bilingual concordancer Reverso Context. After obtaining the subsegment correspondences, a collection of features is extracted from them, which are then used by a binary classifer to obtain the final "GOOD" or "BAD" word-level quality labels. We prepared two submissions for this year's edition of WMT 2015: one using the features produced by our system, and one combining them with the baseline features published by the organisers of the task, which were ranked third and first for the sub-task, respectively.
Abstract.A bitext, or bilingual parallel corpus, consists of two texts, each one in a different language, that are mutual translations. Bitexts are very useful in linguistic engineering because they are used as source of knowledge for different purposes. In this paper we propose a strategy to efficiently compress and use bitexts, saving, not only space, but also processing time when exploiting them. Our strategy is based on a two-level structure for the vocabularies, and on the use of biwords, a pair of associated words, one from each language, as basic symbols to be encoded with an ETDC [2] compressor. The resulting compressed bitext needs around 20% of the space and allows more efficient implementations of the different types of searches and operations that linguistic engineerings need to perform on them. In this paper we discuss and provide results for compression, decompression, different types of searches, and bilingual snippets extraction.
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