Question Answering (QA) in its various flavors has made notable strides in recent years thanks in part to the
availability of public datasets and leaderboards. Large datasets are not representative of many real world scenarios of
interest; this is especially true for industry data and specialized field data. Small datasets cannot be used to train
QA systems from scratch: domain adaptation techniques are required. In this proposal, we use the term domain adaptation
broadly, to cover techniques that leverage out-of-domain data, or in-domain data that does not match the task at
hand.
The workshop is intended to highlight innovative approaches that have the potential to yield significant
improvement in QA scenarios where limited labeled data is available and to promote the development and use of real-world
datasets for domain adaptation.
Topics of interest include established and emerging approaches that have notable potential to substantially impact domain adaptation for QA. Notable examples are: adversarial training, automatic augmentation of a training set, unsupervised transfer learning, joint learning of QA and question generation, multi-task learning, domain-specific knowledge graphs, and using large models with few-shot learning.

The invited talks represent both the industry and the academic perspective: industry has pressing needs for techniques that address the small amount of labeled data that one can expect from customers; academia is leading the path towards innovative breakthroughs that can quickly advance the field. In addition to the invited talks, we will present a case study on a publicly available, IBM created QA dataset.

For more information about this and other IBM events at NeurIPS, follow this link.