Contact the organizers: internlp2022@googlegroups.com
Interactive machine learning studies algorithms that learn from data collected through interaction with a computational agent or human 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. 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.
We leverage the foundation built in the prior workshops to continue to grow the community of researchers whose long-term goal is to develop NLP models that learn from interaction with humans and the world. The goal of this current workshop is to bring together researchers to:
We aim to bring together researchers to share insights on interactive learning from a wide range of NLP-related fields, including, but not limited to, dialogue systems, question answering, summarization, and educational applications. As an emerging sub-field across the NLP and ML communities, a workshop provides the ideal focus and audience size for a vibrant exchange of ideas to help grow the community of interest.
We encourage submissions investigating various dimensions of interactive learning, such as (but not restricted to):