In this talk, I motivate a shift from the traditional Train/Dev/Test
Machine Learning setups, which do not account for concept drift and aim
at a one-size-fits-all solution, towards user-adaptive language
technology that incrementally learns from direct and indirect feedback.
We will take a look at a range of NLP applications that help people
perform tasks and at the same time improve via their usage, allowing
users to personalize them and tune them to their respective needs. This
includes an adaptive annotation tool, a knowledge management browser
plugin and an iterative system for learning text simplification.
Finally, I will discuss some general premises and issues of such
incremental learning systems, motivating interpretability as one
important dimension to facilitate user participation.