Workshop at NeurIPS 2022
Contact the organizers: firstname.lastname@example.org
The 2nd Workshop on Interactive Learning for Natural Language Processing (InterNLP 2022) will be co-located with NeurIPS 2022 and will be held on December 3rd, 2022.
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.
We leverage the foundation built in the prior workshop InterNLP 2021 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:
Previous work has been split across different tracks, task-focused workshops (e.g., Visually Grounded Interaction and Language (ViGIL) workshop at NAACL) and conference venues. These issues have made it hard to disentangle applications from broadly-applicable methodologies or establish common evaluation practices. The NeurIPS 2021 workshop on Human-Centered AI (HCAI) indicates growing interest in interactive AI, but is very broad in scope and does not focus on NLP or language-related interaction. 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):