Exploring Flexibility in Natural Language Generation Through Discursive Analysis of New Textual Genres

Abstract

Since automatic language generation is a task able to enrich applications rooted in most of the language-related areas, from machine translation to interactive dialogue, it seems worthwhile to undertake a strategy focused on enhancing generation system’s adaptability and flexibility. It is our first objective to understand the relation between the factors that contribute to discourse articulation in order to devise the techniques that will generate it. From that point, we want to determine the appropriate methods to automatically learn those factors. The role of genre on this approach remains essential as provider of the stable forms that are required in the discourse to meet certain communicative goals. The arising of new web-based genres and the accessibility of the data due to its digital nature, has prompted us to use reviews in our first attempt to learn the characteristics of their singular non-rigid structure. The process and the preliminary results are explained in the present paper.