Personalised Language Modelling of Screen Characters Using Rich Metadata Annotations

Personalisation of language models for dialogue sensitises them to better capture the speaking patterns of people of specific characteristics, and/or in specific environments. However, rich character annotations are difficult to come by and to successfully leverage. In this work, we release and describe a novel set of manual annotations for 863 speakers from the popular Cornell Movie Dialog Corpus, including features like characteristic quotes and character descriptions, and a set of six automatically extracted metadata for over 95% of the featured films. We perform extensive experiments on two corpora and show that such annotations can be effectively used to personalise language models, reducing perplexity by up to 8.5%. Our method can be applied even zero-shot for speakers for whom no prior training data is available, by relying on combinations of characters' demographic characteristics. Since collecting such metadata is costly, we also contribute a cost-benefit analysis to highlight which annotations were most cost-effective relative to the reduction in perplexity.