A key aspect in the development of statistical translators is the synergic combination of different sources of knowledge. This work describes the effect and implications that would have adding additional other-than-voice information in a voice translation system. In the model discussed the additional information serves as the bases for the log-linear combination of several statistical models. A prototype that implements a real-time speech translation system from Spanish to English that is adapted to specific teaching-related environments is presented. In the scenario of analysis a teacher as speaker giving an educational class could use a real time translation system with foreign students. The teacher could add slides or class notes as additional reference to the voice translation system. Should notes be already translated into the destination language the system could have even more accuracy. We present the theoretical framework of the problem, summarize the overall architecture of the system, show how the system is enhanced with capabilities related to capturing the additional information; and finally present the initial performance results.

A finite-state, rule-based morphological analyser is presented here, within the framework of machine translation system TAVAL. This morphological analyser introduces specific features which are particularly useful for translation, such as the detection and morphological tagging of word groups that act as a single lexical unit for translation purposes. The case where words in one such group are not strictly contiguous is also covered. A brief description of the Spanish-to-Catalan and Catalan-to-Spanish translation system TAVAL is given in the paper.

A new system for statistical natural language translation for languages with similar grammar is introduced. Specifically, it can be used with Romanic Languages, such as French, Spanish or Catalan. The statistical translation uses two sources of information: a language model and a translation model. The language model used is a standard trigram model. A new approach is defined in the translation model. The two main properties of the translation model are: the translation probabilities are computed between groups of words and the alignment between those groups is monotone. That is, the order between the word groups in the source sentence is conserved in the target sentence. Once, the translation model has been defined, we present an algorithm to infer its parameters from training samples. The translation process is carried out with an efficient algorithm based on stack-decoding. Finally, we present some translation results from Catalan to Spanish and compare our model with other conventional models.