Juan Antonio Pérez-Ortiz

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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(More)
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,(More)
In this paper, we extensively evaluate a new hybridisation approach consisting of enriching the phrase table of a phrase-based statistical machine translation system with bilingual phrase pairs matching transfer rules and dictionary entries from a shallow-transfer rulebased machine translation system. The experiments conducted show an improvement in(More)
By the time Machine Translation Summit X is held in September 2005, our group will have released an open-source machine translation toolbox as part of a large government-funded project involving four universities and three linguistic technology companies from Spain. The machine translation toolbox, which will most likely be released under a GPL-like license(More)
Most successful machine translation systems built until now use proprietary software and data, and are either distributed as commercial products or are accessible on the net with some restrictions. This kind of machine translation systems are regarded by most professional translators and researchers as closed and static products which cannot be adapted or(More)
This paper explores a new approach to help non-expert users with no background in linguistics to add new words to a monolingual dictionary in a rule-based machine translation system. Our method aims at choosing the correct paradigm which explains not only the particular surface form introduced by the user, but also the rest of inflected forms of the word. A(More)
The long short-term memory (LSTM) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the(More)
To produce fast, reasonably intelligible and easily corrected translations between related languages, it suffices to use a machine translation strategy which uses shallow parsing techniques to refine what would usually be called word-for-word machine translation. This paper describes the application of shallow parsing techniques (morphological analysis,(More)
Some machine translation services like Google Ajax Language API have become very popular as they make the collaboratively created contents of the web 2.0 available to speakers of many languages. One of the keys of its success is its clear and easy-to-use application programming interface (API) and a scalable and reliable service. This paper describes a(More)
This paper describes the machine translation (MT) system developed by the Transducens Research Group, from Universitat d’Alacant, Spain, for the WMT 2011 shared translation task. We submitted a hybrid system for the Spanish–English language pair consisting of a phrase-based statistical MT system whose phrase table was enriched with bilingual phrase pairs(More)