Abstract

The way we express ourselves is heavily influenced by our demographic background. I.e., we don’t expect teenagers to talk the same way as retirees. Natural Language Processing (NLP) models, however, are based on a small demographic sample and approach all language as uniform. As a result, NLP models perform worse on language from demographic groups that differ from the training data, i.e., they encode a demographic bias. This bias harms performance and can disadvantage entire user groups.

Sociolinguistics has long investigated the interplay of demographic factors and language use, and it seems likely that the same factors are also present in the data we use to train NLP systems.

In this talk, I will show how we can combine statistical NLP methods and sociolinguistic theories to the benefit of both fields. I present ongoing research into large-scale statistical analysis of demographic language variation to detect factors that influence the performance (and fairness) of NLP systems, and how we can incorporate demographic information into statistical models to address both problems.