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Language models that are sensitive to external context can more effectivelycapture the speaking patterns of individuals with specific characteristics orin particular environments. However, obtaining and leveraging such annotationscan be challenging. In this work, we show how to leverage rich character andfilm annotations to personalise language models in a scalable manner. Our bestmodel can reduce perplexity by up to 6.5% compared to a parameter-matchedlanguage model. Our approach performs on par with speaker-specific fine-tuningwhen the fine-tuning data (i.e. past dialogue) for individual speakers isavailable. On top of that, it also generalises well to a scenario with no suchdata, relying on combinations of demographic characteristics expressed viametadata. Our findings are consistent across two corpora, one of which is alsoa contribution of this paper: Cornell-rich contains rich manual annotations for863 speaking characters from the Cornell Movie Dialog Corpus, includingfeatures such as characteristic quotes and character descriptions, along withsix automatically extracted metadata features for over 95% of the featuredfilms. Finally, we also present a cost-benefit analysis highlighting whichannotations are most cost-effective in reducing perplexity.

Personalised Language Modelling of Screen Characters Using Rich Metadata Annotations