A connectionist approach to machine translation

Connectionist Models can be considered as an encouraging approach to Example-Based Machine Translation. However, the neural translators developed in the literature are quite complex and require great human effort to classify and prepare training data. This paper presents an effective and more simple text-to-text connectionist translator with which translations from the source to the target language can be directly, automatically and successfully approached. The neural system, which is based on an Elman Simple Recurrent Network, was trained to tackle a simple pseudo-natural Machine Translation task.