Interactive machine learning studies algorithms that learn from data collected through interaction with either a computational or human agent in a shared environment, through feedback on model decisions. In contrast to the common paradigm of supervised learning, IML does not assume access to pre-collected labeled data, thereby decreasing data costs. Instead, it allows systems to improve over time, empowering non-expert users to provide feedback. IML has seen wide success in areas such as video games and recommendation systems.
Although most downstream applications of NLP involve interactions with humans - e.g., via labels, demonstrations, corrections, or evaluation - common NLP models are not built to learn from or adapt to users through interaction. There remains a large research gap that must be closed to enable NLP systems that adapt on-the-fly to the changing needs of humans and dynamic environments through interaction.