Modelling Referential Choice in Discourse : A Cognitive Calculative Approach and a Neural Network Approach

In this paper, we discuss referential choice – the process of referential device selection made by the speaker in the course of discourse production. We aim at explaining the actual referential choices attested in the discourse sample. Two alternative models of referential choice are discussed. The first approach of Kibrik (1996, 1999, 2000) is the cognitive calculative approach. It suggests that referential choice depends on the referent’s current activation score in the speaker’s working memory. The activation score can be calculated as a sum of numeric contributions of individual activation factors, such as distance to the antecedent, protagonisthood, and the like. Thus, a predictive dependency between the activation factors and referential choice is proposed in this approach. This approach is cognitively motivated and allows one to offer generalization about the cognitive system of working memory. The calculative approach, however, cannot address non-linear interdependencies between different factors. For this reason we developed a mathematically more sophisticated neural network approach to the same set of data. We trained feed-forward networks on the data. They classified up to all but 4 instances correctly with respect to the actual referential choice. A pruning procedure allowed to produce a minimal network and revealed that out of ten input factors five were sufficient to predict the data almost correctly, and that the logical structure of the remaining factors can be simplified. This is a pilot study necessary for the preparation of a larger neural network-based study.