M. Asunción Castaño

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Both Neural Networks and Finite-State Models have recently proved to be encouraging approaches to Example-Based Machine Translation. This paper compares the translation performances achieved with the two techniques as well as the corresponding resources required. To this end, both Elman Simple Recurrent Nets and Subsequential Transducers were trained to(More)
The EuTransAll project aims at using example-based approaches for the automatic development of Machine Translation systems accepting text and speech input for limited-domain applications. During the first phase of the project, a speech-translation system that is based on the use of automatically learned subsequential transducers has been built. This paper(More)
There is growing evidence that overweight and obesity increase the risk of certain cancers. Studies in adults support the role of insulin-like growth factors (IGFs) and oestrogens in the pathogeneses of several cancers. We propose that hormone alterations described as risk factors for cancer in obese adults are present in prepubertal obese children. A group(More)
This paper extends previous work exploring the use of Subsequential Transducers to perform speech-input translation in limited-domain tasks. This is done following an integrated approach in which a Subsequential Transducer replaces the input-language model of a conventional speech recognition system, and is used both as language and translation model. This(More)
The use of internal DC wattmeters, connected to the ATX lines that distribute power from the supply unit to the computer components, is a luring method to profile power in server configurations due to the accurate and complete information provided by this approach. In this paper we enhance the appeal of this type of power meters by addressing one of their(More)