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

TL;DR

The degree to which professional translations in the domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model is suggested, which is also reflected in the contextual model’s superior reference-based scores.

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 6.5% compared to a non-contextual model, and generalise well to a scenario with no speaker-specific data, relying on combinations of demographic characteristics expressed via metadata. Our findings are consistent across two corpora, one of which (Cornell-rich) is also a contribution of this paper. We then use our personalised LMs to measure the co-occurrence of extra-textual context and translation hypotheses in a machine translation setting. Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model's superior reference-based scores.

Figures (5)

• Figure 1: Cornell-rich overview.
• Figure 2: An illustration of the pre-training and fine-tuning regimens used in the experiments.
• Figure 3: sRR illustrated for speaker Hannah.
• Figure 4: Visualisation of a subset of features of the proposed corpus.
• Figure 5: Perplexity reduction from training LMCue with individual speaker attributes.