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Abstract

M. Asunción Castaño, Francisco Casacuberta, Enrique Vidal. Machine Translation using Neural networks and Finite-State Models. 7th International Conference on Theoretical and Methodological Issues in Machine Translation. TMI'97, 1997. pp. 160-167.

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 tackle a simple pseudo-natural machine translation task.