User-factor adaptation is the problem of adapting NLP models to real-valued human attributes, or factors, that capture fine-grained differences between individuals. These factors can include both known factors (e.g. demographics, personality) and latent factors that can be inferred simply from an unlabeled collection of a person’s tweets. Our approach to user-factor adaptation is similar to feature augmentation, a common technique in domain adaptation, with the addition of being able to adapt to continuous variables. We find that we can improve on popular NLP tasks by putting language back into its human context.
To encourage further research, we provide some of the datasets we used, code to generate factors for Twitter users, and a complete stance detection system with user-factor adaptation.
Get them on GitHub.
Veronica E. Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian, and H. Andrew Schwartz
In EMNLP 2017
For questions, please contact Veronica Lynn.