Reference-less Analysis of Context Specificity in Translation with Personalised Language Models

arXiv:2303.16618v3 Announce Type: replace-cross
Abstract: Sensitising language models (LMs) to external context helps them to more effectively capture the speaking patterns of individuals with specific characteristics or in particular environments. This work investigates to what extent rich character and film annotations can be leveraged to personalise LMs in a scalable manner. We then explore the use of such models in evaluating context specificity in machine translation. We build LMs which leverage rich contextual information to reduce perplexity by up to …

abstract analysis annotations arxiv context cs.ai cs.cl cs.lg environments film language language models lms patterns personalised reference speaking specificity them translation type work