Prodigy: Probabilistic Deep Generation Case for Support

Computational methods for generating language are lagging behind computational methods for analysing language in several ways, most obviously in that they have barely been used commercially. The main reasons for this are that systems for generating language take inordinate amounts of time to build, yet once built cannot be reused, and tend to be severely lacking in language variation which is easily perceived as lacking in quality. The current situation in language generation research is reminiscent of language analysis research in the late 1980s, when symbolic and statistical methods briefly formed entirely separate research paradigms. Language analysis soon moved towards a paradigm merger, realising that symbolic methods lacked the efficiency and robustness that probabilistic methods could provide, which in turn would benefit from the accuracy and subtlety of symbolic methods. A similar development is currently underway in the field of machine translation where — after several years of purely statistical methods dominating the field — researchers are now beginning to bring linguistic knowledge back in. The experience from these research fields suggests that higher quality can be achieved when the symbolic and statistical paradigms join forces. Recent research shows that this is likely to be true for language generation too. The aim of the Prodigy project is to develop, for the first time, a comprehensive, linguistically informed, probabilistic methodology for generating language that substantially improves development time, reusability and variation in language generation systems, and thereby enhances their commercial viability.