Perspective-based referring expression generation in a 3D environment
With the ability of communicating verbally that human beings have, a series of problems arises that have no precedents in other animals. Due to differences in customs, origins, sex, race and many others, the way humans express themselves and analize situations can be very different from one person to another.
This project is based on a previous work (Rabadan and Rodriguez, 2014), where it was researched the way in which people create descriptions to refer to someone in particular and a meta-algorithm was created, capable of generating a description of a person in digital 3D environment. Nevertheless, this meta-algorithm had limitations because it was tested in very specific ways, for example, having the person that is going to be described always in the user's field of view, and having the user's field of view fixed and unmovable.
In this project is investigated how spatial properties and the different points of view of the interlocutors affect the composition of descriptions used when referring to another person.
Having as a base the algorithm of the previous project, a new metaalgorithm has been created capable of the generation of descriptions in more realistic circumstances. The performance of the previous algorithm was conditioned by not very realistic factors, but the new meta-algorihtm aims to generate clear and understandable descriptions for human beings and also aims to be prepared to generate descriptions in situations more like real life.
Using the graphic engine Unity 3D, the previous closed room with characters was employed for testing the new meta-algorithm. Nevertheless, the ability to move around the environment and new random characters were added. This way, a 3D environment was created where the different situations the new meta-algorithm can be tested against increases.
The effectiveness of the new meta-algoritm was put to the test by a series of surveys that measure the performace through real life users. These users read the generated drescriptions and look for the character that was described. This way, it was possible to verify that the descriptions were clear enough for any person to understand.
As a consecuence of this project, an advance in the field of Generation of Referring Expressions was made with the creation of a meta-algorithm with a high success rate, capable of adapting in real time and taking into account the relative positions of the description's receptor. This way, the generated descriptions are more realistic than in the previous project.