Cluster-based Prediction of User Ratings for Stylistic Surface Realisation
Nina Dethlefs, Heriberto Cuayáhuitl, Helen Hastie, Verena Rieser, Oliver Lemon · 2014
Surface realisations typically depend on their target style and audience.A challenge in estimating a stylistic realiser from data is that humans vary significantly in their subjective perceptions of linguistic forms and styles, leading to almost no correlation between ratings of the same utterance.We address this problem in two steps.First, we estimate a mapping function between the linguistic features of a corpus of utterances and their human style ratings.Users are partitioned into clusters based on the similarity of their ratings, so that ratings for new utterances can be estimated, even for new, unknown users.In a second step, the estimated model is used to re-rank the outputs of a number of surface realisers to produce stylistically adaptive output.Results confirm that the generated styles are recognisable to human judges and that predictive models based on clusters of users lead to better rating predictions than models based on an average population of users.