Cluster-based prediction of user ratings for stylistic surface realisation

Nina Dethlefs, Heriberto Cuayáhuitl, Helen Hastie, Verena Rieser, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
Pages702-711
Number of pages10
ISBN (Print)9781937284787
DOIs
Publication statusPublished - 1 Apr 2014
Event14th Conference of the European Chapter of the Association for Computational Linguistics - Chalmers University, Gothenburg, Sweden
Duration: 26 Apr 201430 Apr 2014
http://eacl2014.org/

Conference

Conference14th Conference of the European Chapter of the Association for Computational Linguistics
Abbreviated titleEACL 2014
Country/TerritorySweden
CityGothenburg
Period26/04/1430/04/14
Internet address

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