ECAI 2004 Conference Paper

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Improvements on Automatic Word Codification for Machine Translation

Gustavo A. Casañ, M. Asunción Castaño

Encouragingly accurate translations have recently been obtained using a connectionist translator called RECONTRA (Recurrent Connectionist Translator). In order to deal with tasks of medium or large vocabularies, distributed representations of the lexicons are required in this translator. A simple connectionist model has been recently designed to automatically obtain word distributed representations. In this paper several learning algorithms were used to train this connectionist encoder aiming to improve the translation rates achieved with the corresponding obtained codifications of the vocabularies involved.

Keywords: Machine Translation, Automatic Word Encoder, Neural Networks, Training algorithms

Citation: Gustavo A. Casañ, M. Asunción Castaño: Improvements on Automatic Word Codification for Machine Translation. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.576-580.

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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).