Call for Papers
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.
As the impact of machine learning on all aspects of our lives continues to grow, the need for systems that learn through interaction with users and the world becomes more and more pressing. Unfortunately, much of the recent success of NLP relies on large datasets and extensive compute resources to train and fine-tune models, which then remain fixed. This leaves a research gap for systems that adapt to the changing needs of individual users or allow users to continually correct errors as they emerge. Learning from user interaction is crucial for tasks that require a high grade of personalization and for rapidly changing or complex, multi-step tasks where collecting and annotating large datasets is not feasible, but an informed user can provide guidance.
We define Interactive Learning for NLP as training, fine-tuning or otherwise adapting an NLP model to inputs from a human user or teacher. Relevant approaches range from active learning with a human in the loop, to training with implicit user feedback (e.g. clicks), dialogue systems that adapt to user utterances, and training with new forms of human input. Interactive learning is the converse of passive learning from datasets collected offline with no human input during the training process. We encourage submissions in the following topics, including but not limited to:
**Detailed information will follow soon**