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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)
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)
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)
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)
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)
The objective of interactive translation prediction (ITP) is to assist human translators in the translation of texts by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. In this(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)
This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the classical offline grammatical inference with neural networks. The results obtained show that the performance of recurrent networks working online is acceptable when sequences come(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 rule-based machine translation system. The experiments conducted show an improvement in(More)