Paper: Cluster-based Prediction of User Ratings for Stylistic Surface Realisation

ACL ID E14-1074
Title Cluster-based Prediction of User Ratings for Stylistic Surface Realisation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 sub- jective perceptions of linguistic forms and styles, leading to almost no correlation be- tween ratings of the same utterance. We ad- dress 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 sim- ilarity 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 out- puts of a number of surface realisers to pro- duce stylistically adaptive output. Results confirm th...