Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks
It addresses the challenge of making QA systems effective in specialized domains, which is incremental as it applies existing methods to a new context.
This paper tackled the problem of adapting question answering systems to new specialized domains with limited data, demonstrating that standard DNN transfer learning techniques work surprisingly well in the automobile manual domain.
This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using deep neural network (DNN) technology have proven effective for answering general purpose factoid-style questions. However, current general purpose DNN models tend to be ineffective for use in new specialized domains. This paper explores the effectiveness of transfer learning techniques for this problem. In experiments on question answering in the automobile manual domain we demonstrate that standard DNN transfer learning techniques work surprisingly well in adapting DNN models to a new domain using limited amounts of annotated training data in the new domain.