Machine Reading Comprehension (MRC) Questions Answering (QA) systems are commonly used within conversational agents and search engines to support users information needs while saving users the effort of navigation in documents, when the information need is a question for which the user seeks an answer. While state of the art approaches have shown to be successful for QA on a general domain, enterprise retrieval problems where the information need for QA exists in domains that are specialized and have limited or none annotated data remain open.
In this work we address adaptation to new specialized domains with very little training data for MRC-QA, focusing on importance weighting. We propose two features for importance weighting that are applicable for an unsupervised setting, and present preliminary results comparing importance weighting with transfer learning.
