Input Seed Features for Guiding the Generation Process: A Statistical Approach for Spanish

In this paper we analyse a statistical approach for generating Spanish sentences focused on the surface realisation stage guided by an input seed feature. This seed feature can be anything such as a word, a phoneme, a sentiment, etc. Our approach attempts to maximise the appearance of words with that seed feature along the sentence. It follows three steps: first we train a language model over a corpus; then we obtain a bag of words having that concrete seed feature; and finally a sentence is generated based on both, the language model and the bag of words. Depending on the selected seed feature, this kind of sentences can be useful for a wide range of applications. In particular, we have focused our experiments on generating sentences in order to reinforce the phoneme pronunciation for dyslalia disorder. Automatic generated sentences have been evaluated manually obtaining good results in newly generated meaningful sentences.